Part1 state-email

CONSULANT BIO

Pegg Nadler is a marketing professional with more than thirty years in media, nonprofit, publishing and
retail industries spearheading database marketing and direct marketing strategies. Pegg was the first
database marketer to be named Direct Marketer of the Year by Target Marketing magazine in October
2009. In November 2012, she will receive a Silver Apple from the Direct Marketing Club of New York for
her professional contributions to the DM industry.

Pegg is president of Pegg Nadler Associates, Inc. (PNA), a consulting firm that provides database
marketing and direct marketing solutions to clients. For the past fifteen years, PNA has advised
companies on developing marketing database systems, revamping direct response programs and
restructuring business operations for improved marketing, sales and database performance. Services
include business and product development, relationship marketing and integrated marketing, all as
database-driven initiatives. PNA offers customized client seminars and trainings on best practices in
direct response and database marketing.

Previously, Pegg oversaw database marketing operations at Hachette Filipacchi Media US, Consumer
Reports, Phillips Publishing International and the Smithsonian Institution. She led marketing divisions at
Hadassah, Jindo Furs, The Fur Vault and Belvedere Press. Her database marketing career began at
MetroMail (now Experian) providing data, databases and modeling solutions to the mail order and retail
industries. Pegg launched her direct marketing career at Abrams Books developing products for book
clubs, limited edition publishers, continuity programs and catalog companies.

Pegg is the immediate past president of The Direct Marketing Club of New York and serves on the DMA’s
Ethics Policy Committee as well as its Annual Conference Planning Committee. She is former Chair of
the DMA Nonprofit Federation Advisory Council. As an adjunct faculty member, she taught database
marketing at the undergraduate, graduate and professional level programs for New York University and
Baruch College, CUNY. She is a frequent keynote speaker at national and international conferences on
the uses and abuses of database marketing. Her database marketing articles have appeared in industry
publications including Target Marketing, Inside Direct Mail, DMAW Advents, DMCNY Postings and the
DMA Nonprofit Federation Journal.

Pegg has a BA from The University of Albany. She can be reached at pegg@peggnadler.com or at 212-
861-0846.

October 2009

targetmarketingmag.com

COVER STORY

Direct Marketer of the Year:

Pegg
Nadler
Vice President, Database Marketing,
Hachette Filipacchi Media U.S.

P
egg Nadler loves the unknown. Where others see challenges, she sees
Making sense opportunities. Where others fear change, she fears boredom.
and dollars out These are some of the qualities that have driven her 30-year direct
marketing career, the bulk of which she’s spent advancing database marketing
of database operations at commercial and nonprofit organizations and giving back to the
marketing direct marketing community. And they’re why she’s Target Marketing magazine’s
Direct Marketer of the Year.
Speaking over the telephone on a recent Friday evening from her New York
By Heather Fletcher office, the vice president of database marketing for magazine publishing empire
Hachette Filipacchi Media U.S. (HFMUS) quotes a saying from Hungarian Nobel
laureate Albert von Szent-Györgyi Nagyrapolt that has verbally captured her
world view since she studied English and art history at the University at Albany,
State University of New York: “Discovery consists of seeing what everybody has
seen and thinking what nobody has thought.”
“My approach to problem solving has actually always been the same,” Nadler
says. “And it’s interesting how some people will find this a good approach and
others will find that it could be maddening. It has always been very important
for me to see the total scope of business in order to come to a decision. And this
is probably one of the reasons why I love database marketing—because it really
provides that wide picture.”

Falling Into Love
Nadler began fusing her left and right hemispheres early.
The English and art history major entered direct marketing in 1979 by selling
art and gift books for Harry N. Abrams.
“I fell into direct marketing,” Nadler says. “When I came to New York in
the late ’70s, I landed a job at Harry Abrams … and I was first their advertising

which she supervises. So when she accepts
a new challenge, which is usually “directing
startup operations, restructuring business
operations and overhauling marketing
departments,” she is either in charge of or
overseeing every aspect of the solution.
“I’ve always been the person who can
see the large business application and put
the database together and then bring in the
analytical people who will do the number
crunching,” she says. “So I’m really a market-
er who moved into database marketing. …
While I’ve spent all these years doing direct
and database marketing, in my heart of hearts
I’m a marketing, product-development,
business-development person.”
Since diving headfirst into database
marketing in 1990, Nadler steadily has
created and overhauled database systems
and operations for some of the mightiest
corporations and nonprofits in the country.
Each situation is different and requires
her to pull from her well-rounded direct
marketing background as a vendor, con-
sultant and client in the commercial and
nonprofit worlds.
For instance, during the time she spent as
a consultant at the Smithsonian Institution
providing in-house database marketing
expertise, Nadler managed operations first
as a marketing database manager from
1992 to 1993, then as a marketing strat-
egy director from 1993 to 1995. In that
manager and then moved into an area and mailed catalogs. Catering to the jet set, capacity, she analyzed the institution’s
COVER STORY PHOTOS: PAUL GODWIN PHOTOGRAPHY, NEW YORK, N.Y.

called special sales, which was selling books Jindo placed computer terminals at kiosks varied constituencies, including current
into areas other than bookstores. And … in airport waiting areas so passengers could and lapsed audiences.
really it was direct marketing: catalogs, click to buy minks before boarding. Identifying those high-value donor
book clubs, continuity programs. That But her first taste of database market- prospects, proposing a list revenue pro-
was my first exposure into direct marketing, in 1990 at Metromail Corp. (now gram to double sales within the first year
ing. And I thought that it was a little bit Experian), pulled her in to the direct for rented database names, developing
wacky, but that it was much more fun than marketing specialty. Within 18 months, database user training programs and estab-
selling books into bookstores. And it was she’d secured billings nearing $1 million lishing Smithsonian’s database marketing
something that I then stayed with for the for the marketing information, database conferences probably already sound over-
rest of my life.” and mail production company. whelming.
From 1979 to 1990, her direct mar- “I’ve certainly always been very sys- But wait. There’s more.
keting career progressed from moving tematic,” Nadler says. “My attraction to “Smithsonian had been using the data-
art books to selling facsimile editions of English was that I think that speaking very base, but not really to the best ability,”
ancient manuscripts from the Vatican clearly and getting your message across is Nadler says. “So I came in, made tweaks
Library, then to hawking furs in a mostly an imperative. And probably what has to the database, worked with all of the dif-
pre-Internet, fully mid-animal rights move- attracted me to database marketing is that ferent parts of the Smithsonian Institution
ment era. “So being able to sell through I’ve always … organized … I like to get to really let them realize that they had a
the mail and through the phone became projects done. And it probably is a very very good resource there. My one favorite
very important,” Nadler says of her 1988 neat way of wrapping up the world.” story there at the Smithsonian, and this is
to 1990 stint with Jindo Furs. Creatively really not unique to Smithsonian, is that
working her way around the protester The Problem Solver Smithsonian had a database. It might’ve
problem, she set up an 800 number for Speaking of the global picture, Nadler’s been 9 million [names] when I was there.
customers to call; secured accounts with strengths include all aspects of database And there were names which were not
the Home Shopping Network, Comp-U- marketing—with the exception of in-depth housed on the database, which were in
Card, American Express and Diners Club; statistical modeling, the implementation of each of the development offices, includ-

COVER STORY

‘… with the lowering of processing
and technology costs, we are finally able to really
improve our marketing to where everything is
going to be measurable and really everything’s
going to morph into direct. Which is why we’re
calling it integrated marketing.’
—Pegg Nadler

ing the central development office. And appeared from 1997 to 1999, disappearing the whole political arena, and people
divisions didn’t want to share names. This when Nadler accepted the full-time job will be very honest with you about what
is such a common occurrence. Not only in of re-energizing “the marketing face” of is truly making them unhappy and what
nonprofits, but in corporations: ‘Don’t want Hadassah, a nonprofit, pro-Israel Jewish their aspirations and dreams are. So, as I
you to market to my names. Don’t want women’s organization. After a four-year say, it was a big quantum leap to go from
you to contact my names. Want to keep stint as customer database services direc- consulting back to working in a corporate
these names suppressed.’ And I really had tor for Consumers Union, publisher of environment [at Hachette]. But, as I said, it
to work, very carefully, to demonstrate that Consumer Reports magazine, it was back to was certainly for a really good cause. And
the names that were within these various the milliner in 2004 to get refitted for the it’s been hard. It’s been challenging. And
development offices were most probably consultant hat. not for one day have I been bored.”
also on the main database. The list of companies seeking her advice Grabbing Nadler’s attention for a few
“And by being able to overlay data, bring as a consultant is so long and so filled with moments while she’s implementing data-
all of these names together, we would prob- the “Who’s Who” of brands and nonprof- base operations in an environment she clas-
ably have a much more effective develop- its that it simply reads alphabetically, in sifies as undergoing a revolution can feel
ment strategy if we were able to do that,” small type, on her résumé: AT&T, B’nai like pulling a surgeon out of an operating
she continues. “Because we actually showed B’rith Youth Organization, Corporation for room. (While headlines about the publish-
that the names that were housed in all of Public Broadcasting, Discovery Channel, ing industry have been less than flattering,
these different museums were already on the Hachette Filipacchi Media … reflecting widespread industry trauma—
central database. And once we understood That’s where, in 2005, she met from editorial layoffs to magazines folding
what the total correlation was from one area Hachette’s Philippe Guelton. The HFMUS altogether—Nadler is energized about the
to another, we were able to make a much executive vice president and COO had future. She envisions a personalized multi-
better fundraising pitch.” always wanted to build a database. “He channel experience that’s relevant to the
had established a database when he was consumer. More on that later.)
Marketer for All Seasons running Hachette’s operations in Japan,” “We’re in the process of putting together
Of all the hats she’s worn during her direct Nadler explains. a very strong operation,” she says during a
marketing career, Nadler does have a favor- Guelton hired Nadler as a consultant quick call on a recent Monday, in between
ite. in 2005, and she worked on the Hachette planning and budget meetings and search-
“I love a startup,” Nadler says. “And project for two years, while mixing in ing for a director of analysis and modeling.
once the operation is going well, I’m bored. other consulting projects and adjunct Database operations, she says, are meant
And that’s when I really like to turn it over. professorships at New York University to determine “the new products, businesses
… That’s what I’ve done all along—startup, and Baruch College, City University and services Hachette should be offering.
or revamp or overhaul. … And that’s why of New York. Finally, in 2007, Guelton And that’s the most fun.”
the consultant role is really a very good successfully recruited her to work full “In today’s environment, a rich and fully
role for me, because that’s how I’ve always time for Hachette so she could complete developed database is imperative,” Guelton
thought as I’ve gone into companies. And building and implementing the database relates. “We are more effective in helping
I’ve been with so many different companies operations. our advertisers target their prime audiences
that it really has provided me with a very “The last thing I wanted to do was and ideal prospects and in providing our
good bird’s-eye view. And it’s so important give up my consulting,” she says. “It’s so subscribers with new products and better
to be able to step back and look at what’s much fun to be on the outside looking in services. Since joining us in 2005, Pegg
going on.” and letting people tell you what really is Nadler has been key in leading our efforts
Pegg Nadler Associates Inc. of New York troubling them. Because you’re outside to expand our database capabilities …”

A few of the business leaders who have been influential to Pegg Nadler: Bernice Grossman, Arthur Middleton Hughes, and Don Peppers
and Martha Rogers.

Inf luences he was just aware that suddenly there was tomers differently” by using data to keep
More than just DMRS Group President a movement away from print and that the and grow customer relationships.
Bernice Grossman’s friendship and men- circulation counts weren’t really reflecting That creative rather than facts-only
toring (see sidebar below) and the wisdom accurately how many people were involved approach to database marketing points to
of von Szent-Györgyi Nagyrapolt have with reading or being exposed to a certain the last influencer Nadler mentions: Arthur
provided inspiration to Nadler during her product.” Middleton Hughes. Hughes is the founder
long direct marketing career. To that end, Nadler says nonprofits were of the Database Marketing Institute of Fort
Nadler says her other direct mar- the first organizations to take methodical Lauderdale, Fla., and a senior strategist with
keting influences include Jack Kliger, approaches to understanding their audienc- Burlington, Mass.-based e-mail marketing
former president and CEO of Hachette es, or members. During the ’60s, nonprofits firm e-Dialog. She interprets his stance as
Filipacchi Media U.S. (who, as of press were trouncing commercial enterprises saying that there are two types of database
time, was reportedly taking over as act- with the exception of those like American marketers—constructors, who assemble lists
ing CEO of TV Guide). Chairman of the Express and Reader’s Digest. and successfully build the database, and
Magazine Publishers of America from “What were nonprofits doing early on?” creators, who take those names and turn
2005 to 2007, Kliger took the unpopular Nadler asks. “They were writing down all them into loyal, returning customers.
stance that circulation metrics needed to their donor information on index cards— Finally, in Grossman’s case, the admira-
change and magazine publishers needed the earliest form of database marketing. tion is clearly mutual. Grossman describes
to embrace digital technology instead They got it so soon. … Survival. That was Nadler as a politically savvy “overachiever”
of fighting it. “It is essential, I believe, the only way that they were going to be able who has no use for “fluff” and will work as
that our industry moves to a more timely to keep the funding coming in.” hard as she makes anyone else work.
system of readership measurement— Commercial entities caught on to the “Pegg is a continual learner,” Grossman
a system that shows the connection retention concept later, she says, when says. “She is always asking questions. And
between distribution and readership aggressive acquisition campaigns no longer so, when she’s faced with whatever today’s
more effectively,” according to a tran- worked as easily. Nonprofits, which had surprise is, business surprise, she can go back
script of Kliger’s “MPA Breakfast with been cultivating their existing donor bases to that knowledge store of hers and pull
a Leader” from Dec. 7, 2005. all along and moving them up the giving from it. Also, she’s a really good manager.
“The whole notion of the measurable pyramid one step at a time, served as a les- People work for her for extended periods
audience going beyond what had been the son to corporate America, Nadler says. of time. I think that there’s something to
standard magazine circulation base is actu- Enter the next set of visionaries Nadler be said for being a good manager; I don’t
ally something that Jack Kliger … began cites: Don Peppers and Martha Rogers, think it’s all that easy.
talking about … years ago,” Nadler says. the founding partners of Norwalk, Conn.- “I also think that in the competitive world
“And I think when he first spoke about based customer-centric marketing strategy of database marketing … she’s done extreme-
it, a lot of people thought that he was consultancy Peppers & Rogers Group. ly well because she earned it,” Grossman
just off-base. And he really saw this years Nadler says the duo talks incessantly about adds. “… She has this … strategic ability, as
before a lot of other parts of media and ad one-to-one marketing. Or, as the group’s opposed to a tactical functionality. She’s able
agencies began to glean onto this. I think Web site attests, “treating different cus- to look at the big picture. [The] big picture is,

COVER STORY

‘What I want to accomplish.’ And then she how all business transactions started years consumers receive a lot more spam. Web
can go down and look at all of the different ago. [The transactions like] mom and sites will load instantly, and online video
issues she has to address to see whether or not pop shops knowing what color you liked will load faster and be more fun.
she can accomplish it. … I certainly think and when you went out to buy a dress Moving from the future of direct mar-
it’s helped her move forward.” and what your favorite ice cream flavor keting to its specific future, as married to
was. But, as I said, with the lowering of publishing, Nadler’s excited tone doesn’t
What It Is, What It Was and processing and technology costs, we are change much.
What It Shall Be finally able to really improve our market- “This is the most amazing time to be
Nadler is called on to speak to industry ing to where everything is going to be in what we like to say is publishing media,
leaders and college students alike, and measurable and really everything’s going because it is changing dramatically,” she
often gives them the same introduction to morph into direct. Which is why we’re says. “We’re not talking about evolution
to the craft. calling it integrated marketing. I mean, anymore; this is revolution. And no one
“Direct really demanded a response,” even NYU, in their advanced program knows which species is going to make it
Nadler says of the historical difference for direct marketing, they changed the in this catastrophic collision. Will the
between direct marketing and generic name to integrated marketing to really industry collapse? I don’t think so. I think
advertising. “Because you could actually reflect what was going on.” that what we’re going to be left with will
track who was buying what and when. Measurement and ROI are now para- be a publishing medium that is so dynamic
And, of course, database marketing then mount to marketers, no matter what chan- and so important that it’s going to go be
allowed us to ramp this up a notch, because nel they use, instead of following nebulous that much better.”
we could be tracking what that individual metrics like Web site page views and clicks, So after accomplishing what she set
customer was buying over time. she says. “It means that we’re not talking out to do at Hachette—when database
“I just feel that we’ve made a quantum nonsense anymore. We’re truly talking operations are running smoothly—what
leap, and I actually talk about database sense and dollars.” And advancing technol- will the next decade bring for adventure-
marketing being the great leap backward,” ogy will only make that more important, seeking Nadler? With a full-throated
she says of the current state of database she predicts. Direct mail will survive and laugh, she answers: “I wish I could tell
marketing. “Because I’ve always said that be more relevant, mobile marketing will you. I wish I could tell you that.”
database marketing has allowed us to get grow exponentially, and e-mail market- yy
to that personal level, which, of course, is ing will be more targeted—but not before

Extracurriculars
What does a database marketer do to have a good time? Why, to attend this event and also to lead parts of the event.”
hang out with other database marketers, of course. Boone adds that Nadler remains active with the DMA,
From affiliations with the Direct Marketing Association, the specifically helping shape direct marketing ethical compliance
Direct Marketing Clubs of New York and Washington, D.C., and guidelines.
the John Caples International Awards to her former professor- Nadler does find time to spend with her mentor—Bernice
ships, it might not seem like Nadler has time to do much else. Grossman, president and founder of data marketing consultancy
For instance, Xenia “Senny” Boone, DMA’s senior vice DMRS Group of New York—whom she met 15 years ago at an
president of corporate and social responsibility, harkens back industry event.
to Nadler’s time as chairwoman of the advisory council of the “We usually talk about the various types of software installa-
DMA Nonprofit Federation (DMANF). From 2003 to 2005, tions,” Grossman says. “We talk about different kinds of campaign
Nadler led the committee while Boone was the DMANF execu- management software. We talk about what are the best ways to
tive director. segment and target for ultimate acquisition and retention.We talk
“She really helped shape what we call the [Nonprofit] about data and its value as it relates to enhancing the intelligence
Leadership Summit,” Boone says. “This was one of her brain- of, in her case, subscribers, to be a better marketer.
childs. You can appreciate putting together events could be “… Probably the most recent conversation would’ve been about
stressful, but she always was a believer in the need for senior- the comparative evaluation of various software development cam-
level events for the fundraiser and the marketer for the non- paign management tools and their effectiveness for the marketer,”
profit community and really threw herself into it and really was Grossman continues. When asked if she could reveal that conversa-
committed. And when it came to working with the volunteers tion’s conclusions, she declines. Because they’re friends, Grossman
to get them to the event … she was somebody who would says, she’ll provide Nadler with opinions “confidentially, for which I
pretty much do anything to inspire and cajole and get people charge everybody else.”

Reprinted from Target Marketing® October 2009 © Copyright 2009, North American Publishing Co., Philadelphia PA 19130

2012 DMA Database Marketing Post Intensive
AGENDA

Post Intensive Session on Database Marketing
Developing a 21st Century Database—The Tools, Tactics and Tests to Meet Your Business Needs

Within the past five years, massive changes in data, technology and the web have significantly impacted
the planning, research, marketing and sales processes. Business needs have shifted dramatically with a
focus on faster analysis, broader multi-channel integration and dynamic database information systems.

This nine-hour seminar is designed for the database marketer who is looking to enhance or overhaul
database business operations at their company. The instructor lineup consists of leading industry
professionals who regularly evaluate cutting-edge technologies and best practices in database marketing.
Over the course of two days, attendees will be exposed to current and future systems, trends,
recommendations and pitfalls that lie ahead in the 21st century database marketing landscape.

Day 1 Wednesday, October 17 1:00 – 4:00

Part 1-- 1:00 – 2:00
Marketing ROI: How to Ensure Political, Technical, and Business Success for a Database Project
PEGG NADLER, Pegg Nadler Associates
This session will set a realistic foundation for positioning your database for success within your company.
We will look at war stories and success stories and provide guidance and benchmarks for conducting a
business needs/expectations survey and the justification for the continued investment and deployment in
your marketing database division.

Part 2-- 2:00– 3:00
Re-evaluating Your Marketing Database System: A How To
BERNICE GROSSMAN, DMRS Group
A “check list” of the most important items to review when re-evaluating your marketing database, your
vendor, and the design and attending functionality of your current solution tool. Attendees will be
provided with a proven method of what to look for and how to know what is and is not working. Before
you conclude your marketing database is broken, learn how to answer the key questions that determine
the state of your database.

Part 3-- 3:10 - 4:00
Deadly Sins and the Ten Commandments: How to Achieve Best-Practices Database Content and Key
Metrics Reporting
JIM WHEATON, Wheaton Group
A database is only as good as its content, and bad content always costs you money. There is nothing
glamorous about creating and maintaining best-practices content. Data audits and other forms of quality
assurance are hard work. The same is true about carefully reflecting the nuances of your business and
data when creating dashboards and reports. This session will tell you why all of this, although often
overlooked, is so important for database success.

Day 2 Thursday, October 18 8:30 – 2:45

Part 4— 8:30 – 9:30
A Primer on Database Systems—Deciphering Differences and Determining Directions
MARCUS TEWKSBURY, Experian
There is a myriad of database technologies on the market today—and this session is designed to equip
attendees with the key benchmarks to assess and select marketing systems that meet their company’s
existing and anticipated needs. Included in this overview will be an examination of current marketing
automation application software, including traditional vendors, B2B, CRM systems and Web content
systems.

Part 5—9:30 – 10:45
Leveraging Your Database: Reporting, Templates & Strategic Applications
AL BESSIN, Merkle
Identifying the customer, their wants and needs, and what drives their behavior forms the basis for
successful marketing in today’s business environment. Learn how to create a customer balance sheet;
identify where mistakes are being made; and use findings to drive business transformation. Understand
what media is working by looking at different ways in which results are being reported for online and
offline marketing campaigns. Emphasis will be on determining the most practical and actionable methods
to use including marketing performance, lifetime value and business strategy.

Part 6—11:00 – 12:00
Embedded Intelligence, the Next Generation of Analytics
DOUG NEWELL, Calexus
Historically the vast majority of analytic projects have been one-off efforts. By their very nature, such
hand crafted analytics require substantial investment in planning, production and quality control. They are
so labor-intensive that most organizations lack the resources to take advantage of even their most obvious
analytic opportunities. The next generation of analytics is now being embedded into marketing processes.
This session will show how to create such a system of continuous improvement with every analysis.

Break & Boxed Lunch Pickup 12:00 – 12:15 (Boxed Lunch—we will have a working lunch during Part
7)

Part 7—12:15 – 1:15
Navigating the Data Maze
RANDY WATSON, Acxiom
Database marketers are now faced with massive amounts of data, mounting privacy issues and growing
regulations on the use, collection and dissemination of data. This session will look at both traditional and
new sources of data used to shape database analysis programs. We will address the latest trends in
compiled data, co-op databases, online and offline data sources for the B-C and B-B worlds. Best
practices for determining and protecting your customer data needs will be discussed.

Part 8—1:15 – 2:30
Integrating Digital Media Data with Your Marketing Database
RANDY HLAVAC, Lecturer Professor – Northwestern University, Medill IMC (Integrated Marketing
Communications)

Social media, mobile, web communities and other electronic media hold the potential for providing new,
high impact data to improve the ability of our marketing database systems to drive highly targeted CRM
and electronic programs. But challenges exist using this data. What data is important (and legal) to add to
your database? How do we monitor and assess data quality and impact? How do we entice visitors to
provide data? We will examine how to integrate your social, mobile, web, and CRM marketing efforts
into a single Social CRM system.

Part 9--2:30 – 2:45
Database Intensive Wrap Up
Review, Q&A, General Discussion

9/18/2012

Marketing ROI:
How to Ensure Political, Technical,
and Business Success for a Database Project

DMA Database Post Intensive

Presented by Pegg Nadler, President
Pegg Nadler Associates Inc.

Pegg Nadler: Background
• Database marketing consultant specializing in media, nonprofit,
publishing and retail industries.

• Experience: Headed DB operations at Smithsonian, Phillips Publishing,
Consumers Union, HFMUS. Former National Accounts Manager at
Metromail (now Experian). Ran marketing operations at Abrams Books,
Belvedere Press, The Fur Vault, Jindo Furs, Hadassah

• Clients include AT&T, China Post, Corporation for Public Broadcasting,
Discovery Channel, DMA, HFMUS, Smithsonian, Thirteen.org, Time Life
Books, US News & World Report

• Professional Associations: Direct Marketing Club of New York Past
President, DMA Ethics Policy Committee Member, DMA Annual Planning
Conference Program Advisor, DMANF former Advisory Council Chair

1

9/18/2012

Today’s Presentation

• The Changing Business Landscape
• Keys to Database Success
• War Stories
• Success Stories
• Lessons Learned
• Recommendations

3

The New Business Reality
Integrated marketing
communications
Real time analytics & product
offerings

Data generation explosion

Growth of online, mobile &
social media

Audience fragmentation

Databases as key drivers to
revenue

4

2

9/18/2012

Challenges Still Exist

Measurement is Managing the
critical but customer multi-
knowing what to channel
measure & how to experience is a
measure is a key priority
investment theme

Today’s customer
databases are insufficient
to deliver the insight
needed

Top Concerns

Marketing’s Push to
changing reduce costs Integrate
needs are not internally & technologies
Improve ROI
met by externally across
internal IT channels
departments

3

9/18/2012

The Big Question

How do we convince management to
invest or reinvest in the database?

7

What is Key to Database Success?

A “decent”
database
system &
The “right” adequate
team of data
players

An “intelligent”
business
strategy

8

4

9/18/2012

#1: Key Business Issues
Begin with an intelligent business strategy

Not data, not technology, not tools

What decisions need to be made to be successful?
What questions do you want to answer to drive your sales & marketing
programs?

The competitive advantage comes from how analysis is handled

Address the problem, not the technical solution

9

#2: A Database Champion

Statistically Politically
savvy astute

Technically IT
proficient independent

Database Vendor &
Marketing
system
expert Leader knowledge

10

5

9/18/2012

#3: The Right Team

Statistically Politically
savvy astute DB
Modelers Leader

Technically Senior
proficient DB Management
Analysts Support

Database DB Vendor &
Marketing
system
experts Team experts

11

#4: Top Management’s Commitment
Initial & ongoing
financial
backing

The Big C’s—
CEO, CMO,
COO, CFO,
CTO
Mandatory People
compliance & power—
participation in DB personnel for
projects staffing

6

9/18/2012

#5: A Decent Database

Robust
systems &
capabilities

Easy access Budget to
to data by support
database ongoing
team operations

Adequate &
Timely comprehensive
updates
data

13

War Stories: Multi-Product Company
DB manager, no
Opposition to use DB Modeling programs
staff, multiple users
by various slow to test and/or
with little training
departments rollout
around the company

Little DB knowledge, Little or no funding
no standardized for email, online or DB staff reductions
business rules social data

Lack of management IT drives DB vendor A failed database
commitment selection & build project

Inadequate Funding Questionable ROI

14

7

9/18/2012

Lessons Learned

• Absence of dedicated trained staff undermined project
No time for novices success

• No commitment from top C’s to override lack of DB
Big Guns Support cooperation throughout company.

• Top C’s thought they could save $$ by using IT—major
The Black Hole of IT mistakes since IT does not know marketing

• Penny wise & pound foolish—the company must commit
Money in the Bank adequate $$ to fund project properly

War Stories: Membership Organization
Data &
DB initiatives
capabilities ROI unproven
driven by CEO
concerns

CEO hires DB Internal
New DB RFP
director modeling team
issued
hired

Limited Lack of DB No budget
experience DB knowledge approval
director across company

Fulfillment
vendor used as “Black box” DB project
DB system models stalled
provider

16

8

9/18/2012

Lessons Learned

• Don’t let your CEO or management team hire an
No time to be Green inexperienced database director.

Penny wise • You get what you pay for. Spend what you need to hire
expertise.
Pound foolish

• Your fulfillment company should not serve as your
Experience counts database vendor. Find a DB provider with the expertise
and services you require.

• Transparency in operations, analysis and modeling
Information is power methodologies are necessary to encourage DB
confidence, participation and success across a company.

Do Your Homework • Get the DB RFP requirements right the first time.

Success Story: Hearst Magazines

No DB, use
DB build begins ROI plan detailed
Fulfillment System

Modeling & Analytics Program test and
VP DB Marketing
done using disparate rollouts begin
hired
systems

Ongoing investment
Senior Management Commitment to to improve DB &
Team makes modeling & analytics marketing & real time
commitment to DBM
online capabilities

Online & offline data
Select DB vendor
integration

18

9

9/18/2012

Lessons Learned

• Big C’s commitment to DB marketing
Big C’s Support for the short and long term success of
the company

Business • Company objectives and goals clearly
Intelligence defined

DB Partnership • Build with MDB experts, not IT experts

• Hiring a competent DB champion
DB Champion accounts for a quick start and continued
success in DB programs

Demonstrating ROI: Hearst

Projected DB Investment Actual ROI

• Planned for 200% ROI in 3 years • DB paid for itself in one year

• Increased mail efficiency, higher • Consolidating information, getting
customer response rates, clean data, buying better
reduced marketing execution demographics and using online
resources information for DM efforts

• 30% more revenue from internet- • Resulted in 25-30% offline
sold subscriptions response lift

• New models to produce 5% lift on • The database enabled reduction
response for mail on outside lists by around 30%

10

9/18/2012

Taking Inventory
What are your company’s business and customer objectives?

What obstacles are in the way?

What data and campaign information can you not integrate today?

What systems capture customer data across the company?

What is happening across the company that was not included in the initial DB build?

What is done in marketing, research, digital, social, editorial, customer
service, email, mobile and finance?

21

Building the Case for Senior Management

Gather case studies & success stories that pertain
to your particular business & industry

Identify quick wins & gains vs. a long term
detailed plan

Determine a reasonable budget for funding &
operations

When necessary, think small using test databases
& prototypes to gain approval

Don’t overbuild—meet your current & near future
needs since technology & business change

22

11

9/18/2012

Critical Areas for Database Success

A Decent
Senior Database &
Management Adequate
Commitment Data
The Right
Team of
Players
A Savvy
Database
Champion

Key
Business
Issues
Identified

Questions?

Thank you so very much!
Please feel free to reach me at:

Pegg Nadler
President
Pegg Nadler Associates, Inc.
212-861-0846
pegg@peggnadler.com

24

12

Bernice Grossman, DMCNY 2001 Silver Apple Award recipient, Vice Chair
of the Marketing Technology Council and Board Member of the ECHO
Academy of Direct Marketing Arts & Sciences, former Chair of the DMA
B-to-B Council, and member of Who’s Who in B-to-B Marketing created
DMRS Group, Inc. (DMRS) in 1983 to be an independent marketing
database consultancy that determines the complete scope of a customer's
project; "architects" the solution, and administrates the vendor solution that
integrates all of the systems to deliver marketing databases (MDB’s) that
have contributed heavily to the success of leading national marketing
programs. (www.dmrsgroup.com)

DMRS assists companies to better manage their marketing information by
showing them how to capture and leverage customer, prospect and suspect
data to best meet marketing’s needs. No matter what channel is used to
generate the data -- mail, internet, call center, social, space, DRTV, etc.,
through the use of a properly designed MDB / CRM enterprise, greater
"reach" is achieved -- and companies can lower acquisition costs and
increase the lifetime value of each and every customer

Bernice is a noted data expert and can testify in the US Court System on data
theft, fraud, and abuse; she is frequently retained to serve in an advisory
capacity on merger and acquisition projects where the data asset needs to
quantified and monetized.

She is a frequent speaker for The DMA, National Center For Database
Marketing, and Direct Marketing Business and Industry Conferences,
DMAW, DMCNY, and NYU’s Direct Marketing Program, among others.
Prior to starting DMRS, Bernice held key direct marketing / marketing
systems positions at AMI Industries, Inc., ABS, McGraw-Hill, and
Scholastic Inc.

Clients on the DMRS roster have included Avis, Chase Manhattan
Mortgage, Coca Cola, Epson, Kansas City Power & Light, Microsoft, Nestle
Food Services, McGraw-Hill, MTV, Pfizer, Simon Property Group, and
United Airlines.

Ms. Grossman is a native of New York City. She graduated from Ithaca
College and attended Hunter College Graduate School.

9/18/2012

How to Re-Evaluate Your
MDB
MDB Vendor
Howand/or
to Re-Evaluate
Your MDB,
MDB Functionality
MDB Vendor, and/or
By

MDB Functionality
Bernice Grossman
President
By
DMRS GROUP, Inc.
Bernice Grossman
President
bgrossman@dmrsgroup.com
DMRS GROUP, Inc.
bgrossman@dmrsgroup.com

Who is DMRS ?
• DMRS has been working with client companies to maximize their data
marketing efforts since 1983. We are an independent consultancy, we
own no data, no software, nor any processing services or facilities.

• We manage data audits/assessments and operational needs
assessments:
Choosing the right vendors Data / ETL, MSP / ESP, MDB / CRM, MA
/ SFA Implementation End-user marketing applications for off-line
and on-line

• Our client list spans a broad spectrum of Domestic and International
businesses including Avis, Epson, Microsoft, Pfizer, United Airlines,
Nestle, Simon, United States Gypsum, and United Airlines

1

9/18/2012

This Session
• This session will provide a check list of the most important items to
review when re-evaluating your marketing database, your vendor,
and the design and attending functionality of your current solution
tool.
• Attendees will be provided with a proven method of what to look for
and how to know what is and is not working. Before you conclude
your marketing database is broken, come to this session and learn
the key questions to ask that will help determine the state of your
database.
• Key takeaways:
– What do you need now that you didn't need when your marketing
database was built?
– What about your data?
– How should you review database integration with email and
social media - what exists now that didn't exist at the time of the
build?

First, A Definition
just so that we’re all on the same page
An MDB (Marketing Database) is a single repository for all data identified as
relevant to meet the goals of marketing that are defined as actionable and
accessible for:
• Capturing data from all channels
• Consistent data hygiene and de-duplication rules
• Allows for segmentation and query
• Integrates Direct, E-Mail, Social Media (transactional, web site, call center,
behavioral, attitudinal, events – more)
• Performs complete Campaign Management
• Measures media performance
• Manages multi-channel marketing
• Performs modeling and predicting behavior analyses
• It is read only. It is NOT a contact management system.

2

9/18/2012

IS YOUR MDB “BROKEN”?
• What is “broken”? We’re going to look at a few examples in a moment.
• Length of contract
• When does your contract expire?
• (If inside) Is it time to take it off-site?
• Are you all integrated?
• Does your MDB work?
• What are the metrics you use to decide this?
– Do the MDB counts match the transaction counts?
– Does the geography match
• Who decides that it does or does not work?
• Does anyone want to use it?
• Who? Why?
• Who does not?
• Is marketing grumbling
• Is IT smirking

Some “Broken” Examples
• Pharmaceutical Company
– Kept each drug on a separate MDB – became too expensive – realized
they were paying for certain processes three times but only needed to
“buy” it once

• Membership Organization
– The users were in silos – just like their data
– Change Management was very difficult
– Never contemplated the problems of moving data back and forth
(especially from their SFA to the MDB)

• Large Retail Shopping Installation
– Never thought through how to use the response management
functionality

3

9/18/2012

Is Everything Still the Same at the
MSP?
• Corporate mission statement • System software information
and customer service • Percent of budget applied to
philosophy
R&D
• Total number of staff
• Willingness to provide details
• Key executives
pending litigation
• Ownership information and
organization chart • MDB staff attrition over the last
• Quality control procedures year
from data receipt to MDB • Company privacy policy
update • Primary industries that are
• # of customer support staff served
• # of technical support staff • Number/type of user group
• Customer mix meetings held each year

Are Their Data Center Capabilities Still
the Same?
• Available data center locations
• Back up procedures
• Real-time redundancy (servers, HVAC, etc.)
• Disaster recovery and business continuity procedures
• Contingency for downtime and preventive maintenance
• Physical and data security measures
• Connectivity options
• Service levels for problem reporting and resolution
– Do these meet your needs today?
• Ability to provide support 24 x 7 x 365

4

9/18/2012

What About …………
• Has their client list changed? How?
• What have they done to enhance their look-up tables for
company name, title, first name
• Can their solution now support both your marketing and
contact management/SFA needs? How?
• Have they integrated with an ESP?
– Who?
– How are they integrated?
– Is it really one platform or is it two that are “made” to
look like one?
• How are they integrating Social Media?
• What is available to you in Real Time? WHY do you
need real time?

THE CRITICAL QUESTIONS
• When was the last time your BRD was updated?
• When was the last time you compared your BRD to what
you are receiving? This should be done at least 1x/yr
• When was the last time you looked at your ERD?
• Has the staff that manages your MDB changed?
• What do you need now that you didn't need when your
MDB was built? How old is your MDB?
• Have you reviewed the MDB integration processes with
email and social media issues that didn't exist at the time
of the build?

5

9/18/2012

Do you still have the same
“25 Questions”?
WHAT 25 Questions?
If you had an ideal standard and fresh marketing database,
what questions would you want answered from the data?
But, there are 2 conditions:
• Question must be quantitative!
• Question cannot use a subjective word (e.g. big
or better)!
For example: How many customers who purchase SKU
#123 in Mississippi also purchased SKU #456

Original Business Goals and Functional
Requirements
Business goals Functional requirements
• Become customer-centric by • Provide access for query and analysis by
developing a complete view of the both marketing and sales
customer with all pertinent data • Integrate the mail and email query and
• Increase effectiveness and efficiency campaign management functions.
of acquisition and retention marketing • Provide accurate information on new
with better customer targeting and customers, cost to acquire customers,
campaign management number of inactive customers, migration
• Improve overall ROI by marketing to of customers between value segments
most valuable customers and the cost of migration
• Target individual customers with • Use 3rd party B-to-B data to establish
specific messages designed to best corporate hierarchy links of ownership
meet their needs and firmographic profile info
• Understand customer behavior for • Enhance customer data through the use
each product within channels and of 3rd party for demographics, lifestyles,
across the brands behavioral, attitudinal

6

9/18/2012

Has Your Team Changed?
• Team Champion – Owns the Vision and Articulates it to the Team
• Marketing (all channels)
– Direct mail
– Email
– Telemarketing
– Social Media
– Space
– Acquisition
– Retention
– Product
• Sales
• IT
• Finance
• Legal

HAS YOUR ENVIRONMENT
CHANGED?
THIS IS WHAT IS WAS:

Data locations: Files included
– Oracle data warehouse business-to-business
– Mainframe flat files
– SQL Server consumer
– SalesForce.com US and International data
2,000,000 eligible records customers/prospect
on file. Approx. 50 Gb of
data representing the last full postal address, just
3 years. Growth over the email, some “handles”
next 3 years is expected Estimated # users = 20.
at a rate of 25% per year.
WHAT ABOUT NOW?

7

9/18/2012

What about your data?

• Is it the same or has it changed in scope
• Have you added new products, services,
bought other companies, etc.,
• Have you changed the channels you use
for acquisition and/or retention or the
amount you use of a channel?
• Have you changed data vendors?

Data Sources – Marketing Strategies
Have You Added New Ones or Made Significant
Changes?
DATA MARKETING
• Transactional Files • New Channels
• Email • Different Schedules
• Web Site Data • Re-Organized
• Operations • New Management
• Complaints • Decided to Outsource
• Reviews • Added / Deleted Partners
• Tech Support • Bought / Sold a Company
• Social • Other
• Other

8

9/18/2012

Have You Recently Reviewed..

Your data enhancement sources and methodologies
Have you created a “best record” and are the requirements
still the same
Have you reviewed data standardization and sanitization
routines
What about records with only:
Postal Addresses.
E-Mail Addresses.
Social Media “handles”
What about those record missing “key” data elements

What About……………
• Response time
– Do you need increased speed?
– When was the last time you had the server sized?
• Query capabilities
• Multiple users
– Have you added or deleted users?
• Simultaneous usage
– Has this stayed the same?
• Multiple locations
• Data feeds and updates
– Have you added new ones?

9

9/18/2012

Remember when you ……
• Created validation rules for all of the data feeds
• Developed Appropriate Audit Reports for
Data feeds
Database refreshes
Standard reports
• Developed Reject procedures – and decided what to do
what to do when key checkpoints failed
• Do you still follow those rules??

Created Sanity Checks….
• Standard reports that ran after database
refreshes and database feeds to verify key
metrics
• Threshold reports
If “x” metric exceeds an appropriate number
does a red flag goes up?
Who is advised?
Are the reports still automatically distributed to
the appropriate people?
are those people still at your company?
are the reports read?

10

9/18/2012

THE 8 MUST HAVE’S –
Do You Have More / or Are They
Just Different?
Query
Calculating
Reporting
Direct and E Mail Campaign Management
Social Media Integration
Data Extract
Data Import
Data Mining, Analysis, Tracking & Modeling

Do Any of These Still Exists?
• Disparate platforms ---- not everything is connected

• No common repository to store everything

• Creating selections is just too complicated – almost no
one knows SQL except IT

• Data is still not sanitized, standardized, unduplicated nor
aggregated the same way across all of the sources

• Still no written set of up-to-date business rules

• Sill no written BRD?

11

9/18/2012

Nice to Have or Now Must
Have’s
• Real time access
• Data from files not integrated (by name
and address) with the MDB – integration is
done by an ID
• Social Media “handles” are matched to
email addresses
• Bi-synchronous feed with SFA

What are your users doing?

• What are the work-arounds?
• Might these be the reason your MDB is
“broken”
• How many are there?
• How can you get these to be integrated
into the on-going functionality of the
processes your MSP provides?

12

9/18/2012

Some Final Thoughts
• Politics will always rear it’s ugly head – nothing
changes
• This was a high emotional stressful project and it still
it
• There was high, often undirected, energy and its still
there
• Big questions like, “who really owns the data”, MUST
be answered - this is like a moving target!
• Although there were multiple levels of expectation for
the Master Marketing Database (MDB), have you
finally all agreed? Does this need to be reviewed?

LIST OF PLAYERS IN THIS SPACE IS ENDLESS

Customer Relationship Extract, Transform, Load
Management (CRM) (ETL)

Marketing Automation / Lead
Marketing Service Provider Management
(MSP)

13

B-to-B Technology Industry Prospecting Databases:
A Comparative Analysis of Nine Data Suppliers
By Bernice Grossman and Ruth P. Stevens
July 2012

B-to-B Technology Industry Prospecting Databases: A Comparative Analysis of Nine Data Suppliers

B-to-B Technology Industry Prospecting Databases:
A Comparative Analysis of Nine Data Suppliers
By Bernice Grossman and Ruth P. Stevens
July 2012

Executive Summary
As part of ongoing research on B-to-B data sources available to marketers, this white paper evaluates the volume and accuracy of B-to-B data available to mar-
keters of information technology (IT) products and services. Nine database suppliers participated in this year’s study. Like the results from our analysis of com-
piled and response data sources in years past, data coverage and accuracy varied considerably among vendors. We conclude by urging marketers to source
tech-buyer data from multiple sources to gain maximum market coverage. We also suggest that marketers who order prospecting data ask very carefully about the
nature of the data sources and compilation methods involved. Finally, we recommend that marketers conduct a pre-test of the data to assess its applicability to
their particular marketing need.

Building on the general enthusiasm surrounding our past three studies on the We were very pleased that nine suppliers joined the study, and we extend our
accuracy and completeness of B-to-B compiled and response data, we decided gratitude to them. From those who declined, three reasons surfaced. As with
to conduct similar research on the data available in the large and active last year’s response data study, some managers of response databases felt that
technology marketing sector. only their list-owner clients could make the decision to participate, and the
We found a sizable quantity of suppliers offering compiled data, response data, complexity managing all those permissions was too great. Some database
or a combination, to marketers who are trying to reach technology buyers. owners felt that our methodology favors vendors with large volumes of data,
Invited to participate were: and the strengths of those that compete on quality versus quantity would not
be made evident in our study. We understand both of these lines of reasoning,
n ALC n InsideView and hope we can figure out refinements to our study that will overcome these
n Broadlook n Mardev-DM2 limitations in the future. In the case of a few other vendors, further discussion
n CardBrowser n MeritDirect MeritBase revealed that they do not offer data for rent or append, but instead make it
n D&B n NetProspex available through a proprietary platform—thus being ineligible for inclusion.
n Data.com n ReachForce
Demandbase One relatively unusual aspect of the world of technology marketing is the
n n Stirista
Discoverorg.com proliferation of specialty data providers who dig deep into the characteristics
n n TechTarget
Harte-Hanks of target accounts, particularly among very large enterprises with vast technol-
n n UBM
IDG ogy budgets. These vendors invest in capturing useful information like the
n n Worldata
Infogroup Targeting Solutions specifics of the account’s current installed technology, and their buying
n n ZoomInfo
processes, buying roles, budgets and purchase intentions. These vendors

1


may not offer as many records as others, but each record is very richly detailed. As with our earlier data studies, we asked the vendors to provide company
Examples of such vendors are SalesQuest, iProfile.net, and InsideView. This counts in a selection of target industry sectors, plus contact counts for specific
kind of information is extremely valuable for key account planning. But is a companies, and complete records on individual business people.
considerably different animal from the prospecting databases studied here.
We specified the same ten industries as in prior studies, and asked the vendors
The nine participants who contributed information on their tech-buyer data are: to tell us how many companies they had in each of the ten, as indicated by SIC.
n Data.com n Infogroup n Stirista For the contact data, we made two changes from prior studies. First, we dou-
n D&B n Mardev-DM2 n Worldata bled the number of companies for whom contact counts were requested. While
n Harte-Hanks n NetProspex n ZoomInfo we used the same set of well-known large firms in each of the ten industries as
Our sincere thanks to them, and to everyone else who considered participating. in the 2010 and 2011 studies, we added another list of ten smaller firms, in the
same ten industries, to broaden the understanding of vendor data by company
The scope and intent of the study size. This change we made in response to requests by several readers of past
We followed the same approach as used in our earlier research on compiled studies who are interested in targeting small/medium businesses versus large
and response databases, to get answers to the concerns of business marketers enterprises.
about data volume, completeness and accuracy. By using a similar research
methodology, we also hoped to provide some apples-to-apples comparison Second, to get at the tech-buyer question, we specified that the contact counts
among the contents of response databases, compiled databases, and industry- be limited to IT professional contacts. We offered the participating vendors
specific databases, over time. the following list of technology professional titles, as examples of the types
of contacts we expected them to include in their counts.

Examples of IT Professional Titles

Architects Directors Technology Programmers Systems Analysts
Business Analysts Disaster Recovery Specialists Project Leaders Technology Systems Engineers
CIO's Help Desk Project Managers Technology Systems Managers
Computer Operations Managers Help Desk Managers Quality Assurance Systems Programmers
Computer Operators Infrastructure Analysts Quality Assurance Managers Technical Consultants
CTO's LAN Administrators Sales Support Engineers Technical Liaison
Data Modelers LAN Managers Security Specialists Technical Support
Database Administrators (DBA's) Network Administrators Software Developers Telecommunications
Database Analysts Network Directors Software Development Managers Telecommunications Managers
Database Managers Network Engineers Software Engineers VP's Technology
Datacommunications Network Managers Solution Engineers WAN Administrators
Datacommunications Managers Network Support Solutions / Services - Tech Sales Reps Web Developers
Datawarehouse Architects NOC Specialists Storage - SAN Administrators Web Masters
Desktop Support Managers NOC Team Leaders Systems Administrators Wireless Communications

2


We also recruited ten IT professionals Individual contacts in the study
in a variety of industries, who agreed Industry Name Title Company
to lend their names and contact infor- Communications Michael Green Sr. Manager, Database Marketing Level 3 Communications, LLC
mation. We are grateful for their gen- Electronics Al Logiodice Platform Manager, Store.Sony.com Development Sony Electronics
Financial Services Michael Spencer Director, Information Technology Barclays Capital
erous support of this study.
Healthcare Technology Arthur J Fisher Marketo & SalesLogix Marketing DBA GE Healthcare
We asked only one qualitative ques- Manufacturing Doug Lee Reporting Manager Pasternack Enterprises, Inc.
tion, inviting the vendors to explain Marketing Dan Spiegel Vice President of Engineering AdMarketplace
Not-for Profit Andrew Lazar Senior Technical Business Analyst/Database Developer American Institute of Chemical Engineers
their competitive positioning in the
Optical Equipment Jeff Harvey Director of IT Edmund Optics, Inc.
marketplace. Software Rick Graham President Dual Impact Inc.
Technology Dominic Dimascia VP, Technology Delivery Services GSI Commerce
The positioning statements
Here is how the vendors described themselves in response to the following ensure the accuracy of our data, vetting information through a rigorous quality
question: assurance process, and linking each contact to a unique company identifier, the
Provide a statement of no more than 150 words that describes your tech data D-U-N-S® Number. This connection between contact and company offers key
product/service, including how you are positioned, meaning your competitive insight – such as employee count and sales-- that puts a prospect's technology
differentiation. In short, this question is, “Who are you, and how are you dif- purchase in context. No one else offers this comprehensive view of contacts
ferent?” and the business they’re in.
Data.com Harte-Hanks
Launched in September 2011 at Dreamforce, Salesforce Data.com is democra- Harte-Hanks is the industry’s most trusted source for detailed information and
tizing data by delivering instant access to the business data companies need insight into today’s business technology buying market. Our flagship product,
right inside salesforce.com. We provide the data foundation customers need to the Ci Technology Database™ (CITDB), tracks technology installations, purchase
succeed as a social enterprise by helping them easily find new customers and plans and key decision makers at more than four million locations in 25 countries
clean their data right in the cloud. Data.com delivers the data foundation with in North America, Latin America and Europe. Detailed profiles include:
accurate crowd-sourced contact information and the leading company informa- n Technology purchase plans including budget, need, timing, preferred vendor
tion from Dun & Bradstreet. Data.com draws on a community of over 2 million and key decision-maker.
strong members which make over a million updates a month, all in real-time to n Installed technology and primary manufacturers for more than 45 products
address the pace of change in business data. Data.com stands alone as social, including computer hardware, software, networks, storage and telecommu-
transparent, collaborative and integrated directly in salesforce.com -- powering nications
marketers to grow their business with complete and quality business data. n Site and enterprise-level IT budgets and IT staffing estimates
D&B n Detailed contact information on IT and business decision-makers including
D&B Professional Contacts provides high-quality contact information – includ- functional responsibility.
ing email addresses and direct dials – on more than 60 million U.S. business n Plus, 65 descriptive fields including address, telephone, number of employ-
professionals. Our database includes 900+ standardized job titles spanning sole ees, annual revenue, industry classifications, DUNS number and fiscal year
proprietorships and multi-billion dollar enterprises. Customers selling into IT end. Put the power of the Ci Technology Database to work for you. Contact
organizations have access to IT contacts as well as other business stakeholders the technology experts at Harte-Hanks at 1-800-854-8409 or visit
who may be involved in the purchasing decision. D&B takes rigorous steps to www.citdb.com for more information.
3


Infogroup Targeting Solutions to deliver targeted prospect lists, data cleansing, and profiling analytics that
Infogroup Targeting Solutions helps companies increase sales and customer help to uncover data insight and optimize lead generation results. Voted Best
loyalty through analytically driven consumer and business data and database Lead Generation Solution by the SIIA, NetProspex maintains a deep database
marketing solutions. With exclusive access to the Data AxleTM, we build of millions of crowd-sourced business contacts verified by CleneStep™
multichannel solutions using contextually relevant information on 230MM technology. Thousands of B2B organizations rely on NetProspex to acquire
individuals and 24MM businesses. We incorporate the highest quality, most and maintain clean, accurate prospect information to fuel high-performing
accurate and comprehensive compiled and third-party information rich data. marketing campaigns. More information at www.netprospex.com or on
Our response generated data sources contain millions of records of leading IT Twitter @NetProspex.
executives and professional IT buyers within the US and Canada. Additionally,
Stirista
our B2B response driven powerful databases are rich in IT & technology Quite often the term 'social media' is used as a buzzword, but we rarely see
related buyer information. We provide solutions and services to support practical usage and integration of the data with actionable email addresses.
marketers’ and sales’ efforts throughout the entire marketing and sales cycles Stirista combines information from public profiles and websites and connects
by integrating cross-channel data from disparate sources to provide insights that information with an email database. This helps IT vendors identify exactly
that ultimately increase efficiency, productivity and target the most responsive what technologies and products the IT buyers interested in even before some-
customers and prospects to drive the highest ROI. one makes a pitch to them. By figuring out, for instance, that an IT department
Mardev-DM2 specializes in .NET and is part of an online discussion forum for .NET, one can
Mardevdm2 DecisionMaker® Databases are more than just a masterfile. They safely assume that a conference on Linux would not be of much interest to that
are custom built, multi-channel databases that start with all of our individual, individual. Stirista knows something beyond the fact that someone is an IT
high quality, direct response lists and end with custom built, single-source director and that makes the data exponentially more powerful. It not only
databases that provide marketers with both “deep data” selectivity and larger helps with enhanced targeting capabilities but also decreases the potential
volumes of names. Selectable by specific detailed title and level, buying of lost revenue and time due to incorrect messaging.
authority, software, hardware, number of PCs, laptops and printers as well as
Worldata
other IT related site data. It is this combination of depth, quality and coverage, Worldata is the leading data agency firm in the U.S. As the largest buyer and
that differentiates Decisionmaker from other masterfiles, improving marketing user of 3rd party permissioned email media, Worldata has unique abilities that
outcomes for our varied client-base. Partners include BuyerZone, CFE Media’s our clients leverage including: reduced costs, special data availability and
Consulting Specifying Engineer, Control Engineering, Plant Engineering, overall best practice knowledge. Our primary focus is with the Email, Direct
Financial Media Group, Ward’s Business Directory, IBIS, Lexis Nexis’s Mail and Telemarketing categories. We help marketers to execute prospect
Corporate Directories, Martindale Hubbell, Advertiser and Agency Redbooks, marketing programs, data hygiene initiatives and overall direct marketing
Reed Business Information, RS Means and many other highly reputable strategies. More than 800 customers worldwide from all types of businesses
controlled circulation and media partners. and organizations—from enterprise technology, publishing, and online
NetProspex education to business services, nonprofits, and associations—use Worldata
NetProspex is the only B2B data provider with a proprietary verification to leverage data assets, procure key datasets and find overall solutions to
process to ensure clean, accurate, and up-to-date contact information. customer and prospect data initiatives. For more information contact Jay
NetProspex drives customer acquisition by partnering with B2B marketers Schwedelson at 800.331.8102 x176 JayS@Worldata.com.

4


ZoomInfo sands of contributors who allow us to scan their email signatures in exchange
ZoomInfo is a B2B directory of over 50 million people throughout 5 million for viewing our data. Our missions is to be able to map the business landscape
companies that includes contact information such as phone numbers, email ad- in near real-time, and our technology is close to being able to give business
dresses, and mailing addresses as well as the most in-depth profiles on individ- professionals a 30-day snapshot so that our data is as up-to-date as possible. In
uals. The core of our technology is our patented web-crawling tools which terms of IT titles, our database consists of over 1,814,000 IT titles throughout
help us compile all of our information. We also have a community of thou- 189 industries as well.

The company counts reported
Here are the company counts in each of the ten industries reported by the vendors in response to the question,
State the number of U.S. firms you have on your file in each of these 10 SICs. Also state (Y/N) whether you code firms with NAICS.

SIC 32 56 28 64 73 81 80 82 35 48 Comments

Stone, clay and Apparel and Chemical and Insurance agents, Business Legal Health Educational Machinery, Communi- Do you code firms
glass products accessory stores allied products brokers & services services services services services except electrical cations with NAICS? (Y/N)
Data.com 22,141 8,832 4,946 81,634 164,279 78,184 25,541 17,753 51,298 43,494 Yes
D&B 40,391 308,890 53,049 307,131 5,799,337 488,019 1,454,473 360,850 127,086 200,884 Yes
Harte-Hanks 13,555 2,630 24,803 72,568 372,699 33,784 507,566 216,088 60,140 56,319 Yes
Infogroup 45,355 335,512 59,776 444,584 4,306,799 598,841 2,189,964 457,247 155,251 242,965 Yes
Mardev-DM2 34,080 154,213 45,065 834,340 2,866,125 531,718 955,738 299,494 111,255 111,116 No *
NetProspex 10,049 7,753 14,358 45,909 261,998 46,892 75,625 74,899 45,687 35,761 No **
Stirista 1,937 2,893 12,704 15,296 78,682 29,642 63,639 176,019 15,019 23,668 Yes
Worldata 27,075 172,644 35,490 200,317 3,412,525 439,812 1,203,994 327,309 86,600 137,566 Yes
ZoomInfo 3,813 42,213 23,655 52,906 284,518 80,908 81,689 155,291 19,088 36,431 No ***

* Most of our database participants are response lists and the demographics are self reported. Because of this not all of our records are SIC coded. We have our own detailed business activity to allow our customers to target their marketing efforts.
** SIC codes are available down to the 8-digit level.
*** We have some companies with NAICS codes but not many.

5


The contacts counts reported
Here are the counts for contacts at ten each large and small companies in response to the question,
Provide the total number of contacts with IT-related titles you have at each of these 20 firms, U.S. only, including headquarters and all branch locations.
For a list of the kinds of titles we are interested in, see below (see p. x for the list).

Large enterprises Data.com D&B Harte-Hanks Infogroup Mardev-DM2 NetProspex Stirista Worldata ZoomInfo
Andersen Windows 91 179 25 29 9 45 33 189 17
Nordstroms 238 379 19 494 3 250 983 1,117 90
Monsanto 276 351 95 621 448 453 869 928 289
MetLife 1,665 2,630 241 2,088 1,000 753 2,258 2,287 370
Accenture 9,465 4,610 49 4,826 3,332 3,310 8,052 1,701 2,242
Baker & McKenzie 93 119 22 136 139 177 135 451 44
Methodist Hospital System 107 120 12 53 113 89 155 344 516
ETS (Educational Testing Service) 163 190 4 265 218 217 145 133 27
Dell 1,473 1,756 83 3,928 2,096 1,287 1,512 2,319 3,220
Verizon 776 3,024 551 5,088 6,001 2,683 701 3,879 1,611
Small/medium enterprises
Overly Door Company 3 6 0 4 5 5 4 5 2
Haggar Clothing Company 5 0 40 14 16 10 7 188 9
Frontier Pharmaceutical, Inc. 0 0 0 1 0 0 4 0 0
Hicks Insurance Group 0 0 0 3 0 0 3 0 0
Cadence Management Corporation 0 0 1 2 4 3 2 0 1
Henderson Legal Services, Inc. 0 0 1 1 1 1 2 0 0
Tri-anim Health Services, Inc. 1 0 3 5 6 7 4 9 0
Kumon Learning Centers 0 12 5 101 0 7 15 21 0
Device Technologies, Inc. 1 2 0 112 3 1 5 1 3
Reel-o-Matic, Inc. 0 0 0 4 0 2 2 0 2

6


Complete contacts reported
Here are the figures on complete counts for each industry, in response to the question,
Provide the number of “complete” contact records among the IT professionals you have at each firm. Complete means including full name, address, title,
phone, and email.

Large enterprises Data.com D&B Harte-Hanks Infogroup Mardev-DM2 NetProspex Stirista Worldata ZoomInfo
Andersen Windows 91 169 12 17 2 45 33 167 12
Nordstroms 238 371 3 249 1 250 983 994 40
Monsanto 276 310 30 354 48 453 869 813 113
MetLife 1,665 2,584 139 1,424 166 753 2,258 1,978 254
Accenture 9,465 4,589 19 4,119 269 3,310 8,052 1,432 205
Baker & McKenzie 93 111 11 248 25 177 135 405 14
Methodist Hospital System 107 111 6 194 18 89 155 293 362
ETS (Educational Testing Service) 163 184 2 183 41 217 145 88 1
Dell 1,473 1,699 44 1,298 224 1,287 1,512 2,016 1,171
Verizon 776 2,830 168 1,801 578 2,683 701 3,287 736
Small/medium enterprises
Overly Door Company 3 6 0 2 0 5 4 4 2
Haggar Clothing Company 5 0 39 6 4 10 7 134 1
Frontier Pharmaceutical, Inc. 0 0 0 2 0 0 4 0 0
Hicks Insurance Group 0 0 0 18 0 0 3 0 0
Cadence Management Corporation 0 0 0 1 0 3 2 0 0
Henderson Legal Services, Inc. 0 0 1 1 1 1 2 0 0
Tri-anim Health Services, Inc. 1 0 1 7 0 7 4 6 0
Kumon Learning Centers 0 12 4 94 0 7 15 17 0
Device Technologies, Inc. 1 1 0 23 0 1 5 1 2
Reel-o-Matic, Inc. 0 0 0 4 0 2 2 0 1

7


The contact records reported
Here are the records for our ten individual business people, in response to the following directions. Please pull the record of each of these 10 IT professionals as it
currently appears on your file. Submit the record in its entirety. Note: Please do not use any other data sources (e.g., tele-verification, or Internet search) to re-
search these names. We have secured permission from these 10 people to include their data in this research, and we have told them they will not be contacted or
researched in any way by the participating suppliers.

Contact Record: Dominic Dimascia
First name Last name Title Company Address 1 Address 2 City State Zip Office Phone Email
Correct record Dominic Dimascia VP, Technology Delivery Services GSI Commerce 935 First Avenue King of Prussia PA 19406 (610) 491-7221 dimasciad@gsicommerce.com
Data.com Dominic Dimascia Vice President Technology GSI Commerce, Inc. 935 1st Ave King Of Prussia PA 19406-1342 +1.610.491.7000 dominicd@gsicommerce.com
Delivery Services
D&B Dominic Dimascia Vice President Technology Gsi Commerce, Inc. 935 1st Ave King of Prussia PA 19406 DIMASCIAD@GSICOMMERCE.COM
Delivery Services
Harte-Hanks
Infogroup Dominic Dimascia Vice President Technology GSI Commerce, Inc. 935 1st Ave King Of Prussia PA 19406 610-491-7000
Delivery Services
Mardev-DM2 Dominic Dimascia VICE PRESIDENT TECHNOLOGY GSI Commerce 935 1ST AVE KING OF PRUSSIA PA 19406-1342 610 491 7000
DELIVERY SERVICES
NetProspex Dominic Dimascia Vice President Technology GSI Commerce, Inc. 935 1st Ave King Of Prussia PA 19406-1342 (610) 491-7000 dimasciad@gsicommerce.com
Delivery Services
Sirista Dominic Dimascia eCommerce Executive GSI Commerce, Inc. 935 1st Ave King Of Prussia PA 19406 6104917000 dominicd@gsicommerce.com
Worldata Dominic Dimascia VP, Technology Delivery GSI Commerce, Inc. 935 First Avenue King of Prussia PA 19406 (610) 491-7000 dimasciad@gsicommerce.com
Services at GSI Commerce
ZoomInfo

Contact Record: Arthur Fisher
Correct record Arthur J Fisher Marketo & SalesLogix GE Healthcare 40 IDX Dr South Burlington VT 5407 802-859-6476 jay.fisher@ge.com
Marketing DBA
Data.com
D&B
Harte-Hanks
Infogroup
Mardev-DM2
NetProspex Jay Fisher Database Administrator General Electric Company PO Box 1070 Burlington VT 5402 802 859-6476 jay.fisher@ge.com
Sirista
Worldata
ZoomInfo Arthur Fisher GE Healthcare LTD So. Burlington, Vermont, 800 Centennial Avenue, Piscataway New Jersey 8855 (732) 457-8000
United States P.O. Box 1327

8


-Contact Record: Rick Graham
Correct record Rick Graham President Dual Impact Inc. 241 Forsgate Drive Suite 208 Jamesburg NJ 8831 (732) 656-0745 rick@computercare.com
Data.com Rick Graham I T Department Dual Impact Inc 109 S Main St Cranbury NJ 08512-3174 +1.609.448.4449 rick@computercare.com
D&B Rick Graham President Dual Impact Inc 241 Forsgate Dr Ste 208 Jamesburg NJ 08831 7326560673
Harte-Hanks Rick Graham President Dual Impact Inc 241 Forsgate Dr Ste 208 Jamesburg NJ 08831-1385 (732)656-0673
Infogroup Richard Graham President Dual Impact 241 Forsgate Dr # 208 Jamesburg NJ 08831 732-656-0673 rick@computercare.com
Mardev-DM2 RICHARD GRAHAM PRESIDENT DUAL IMPACT 3762 SUMMER ROSE DR ATLANTA GA 30341-1690 732 656 0673
NetProspex Rich Graham President Computer Care 241 Forsgate Dr. Ste 208 Jamesburg NJ 8831 732-656-0745 rick@computercare.com
Sirista RICK GRAHAM IT DEPARTMENT DUAL IMPACT INC 241 FORSGATE DR STE 208 JAMESBURG NJ 8831 7326560673 rick@computercare.com
Worldata Rick Graham President ComputerCare, Inc. 241 Forsgate Drive Suite 208 Jamesburg NJ 08831 (800) 248-0122 RICK@computercare.com
ZoomInfo

Contact Record: Michael Green
Correct record Michael Green Sr. Manager, Database Level 3 Communications, LLC 100 S Cincinnati Ave Suite 1200 Tulsa OK 74103 918-547-0602 mike.green@level3.com
Marketing
Data.com Michael Green Database Marketing Manager Level 3 Communications, Inc 1025 Eldorado Blvd Boulevard Broomfield CO 80021-8254 +1.720.888.1000 michael.green@level3.com
D&B
Harte-Hanks Mike Green Sr. Manager, Database Level 3 Communications OK (918) 547-0602 Mike.Green@Level3.com
Marketing - Level3
Infogroup
Mardev-DM2
NetProspex Michael Green Database Marketing Manager Level 3 Communications Inc. 1025 ELDORADO BLVD BROOMFIELD CO 80021 (720) 888-1000 michael.green@level3.com*
Sirista MICHAEL GREEN SR. MANAGER, DATABASE LEVEL3 COMMUNICATIONS 100 S CINCINNATI AVE TULSA OK 74103 9185476000 michael.green@level3.com
MARKETING
Worldata Michael Green Senior Manager, Database Level 3 Communications, Inc. 1025 Eldorado Boulevard Broomfield CO 80021 (720) 888-1000 michael.green@level3.com
Marketing
ZoomInfo Mike Green Senior Manager, Database Level 3 Communications, Inc. Tulsa, Oklahoma, 1025 Eldorado Broofield Colorado 80021 (918) 547-0602 mike@level3.com
Marketing United States Boulevard

* Michael Green has opted out of the NetProspex database, but his record is on the file.

9


Contact Record: Jeff Harvey
Correct record Jeff Harvey Director of IT Edmund Optics, Inc. 101 E. Gloucester Pike Barrington NJ 8007 800-363-1992 x6825 JHarvey@edmundoptics.com
Data.com Jeff Harvey Marketing Manager Edmund Optics, Inc. 6464 E Grant Rd Tucson AZ 85715-8801 +1.856.573.6250 x6825 jharvey@edmundoptics.com
D&B Jeff Harvey Marketing Manager Edmund Optics, Inc. Edmund Scientific Co 101 E Gloucester Barrington NJ 08007 8565473488
Pike
Harte-Hanks
Infogroup Jeff Harvey Director of IT Edmunds Optics Inc. 101 E Gloucester Pike Barrington NJ 08007 800-363-1992 jharvey@edmundoptics.com
Mardev-DM2 JEFF HARVEY DIR-IS EDMUND INDUSTRIAL 101 E GLOUCESTER PIKE BARRINGTON NJ 08007-1380 856 573 6250
OPTICS INC
NetProspex Jeff Harvey IT Director Edmund Optics Inc 101 EAST GLOUCESTER PIKE BARRINGTON NJ 8007 (856) 573-6250 jharvey@edmundoptics.com
Sirista JEFF HARVEY DIRECTOR OF IS EDMUND OPTICS, INC. 101 E GLOUCESTER PIKE BARRINGTON NJ 8007 8565736250 jharvey@edmundoptics.com
Worldata Jeff Harvey Director of Information Edmund Optics Inc. 101 East Gloucester Pike Barrington NJ 08007 (800) 363-1992 jharvey@edmundoptics.com
Systems
ZoomInfo

Contact Record: Andrew Lazar
Correct record Andrew Lazar Senior Technical Business Analyst/Database American Institute of Chemical 3 Park Avenue New York NY 10016 646-495-1336 andrl@aiche.org
Developer Engineers
Data.com Andy Lazar Senior Information Technology Support American Institute of Chemical 3 Park Ave New York NY 10016-5901 +1.800.242.4363 andrl@aiche.org
and Director Engineers (AIChE)
D&B
Harte-Hanks
Infogroup
Mardev-DM2 ANDREW LAZAR SENIOR PROFESSIONAL AMERICAN INSTITUTE OF 3 PARK AVE NEW YORK NY 10016-5991 646 495 1377
CHEMICAL ENGINEERS
NetProspex
Sirista ANDREW LAZAR DIRECTOR APPLICATIONS AND DATABASE AMERICAN INSTITUTE OF CHEMICAL 3 PARK AVE FL 19 NEW YORK NY 10016 6464951336 andrl@aiche.org
DEVELOPMENT ENGINEERS (AICHE)
Worldata Andrew Lazar Technical Business Analyst/Database American Institute of Chemical 3 Park Avenue New York NY 10016 (800) 242-4363 andrl@aiche.org
Developer Engineers
ZoomInfo

10


Contact Record: Doug Lee
Correct record Doug Lee Reporting Manager Pasternack Enterprises, Inc. 17802 Fitch Irvine CA 92614 949-261-1920 x139 doug@pasternack.com
Data.com
D&B Doug Lee Reporting Manager Pasternack Enterprises, Inc. 17802 Fitch Irvine CA 92614 8667278376 DOUG@PASTERNACK.COM

Harte-Hanks
Infogroup Doug Lee Pasternack Enterprises Inc 17802 Fitch Irvine CA 92614 949-261-1920
Mardev-DM2 DOUG LEE REPORTING MANAGER PASTERNACK ENTERPRISES INC 17802 FITCH IRVINE CA 92614-6002 949 261 1920
NetProspex Doug Lee Reporting Manager Pasternack Enterprises Inc 1851 Kettering Irvine CA 92614-5617 (949) 261-1920 doug@pasternack.com
Sirista DOUG LEE REPORTING MANAGER PASTERNACK ENTERPRISES, INC. PO BOX 16759 IRVINE CA 92623 8667278376 doug@pasternack.com
Worldata Doug Lee Reporting Manager Pasternack Enterprises, Inc. 17802 Fitch Irvine CA 92614 (949) 261-1920 doug@pasternack.com
ZoomInfo

Contact Record: Al Logiodice
Correct record Al Logiodice Platform Manager, Sony Electronics 16500 Via Esprillo San Diego CA 92127 858-942-5347 al.logiodice@am.sony.com
Store.Sony.com Development
Data.com
D&B
Harte-Hanks
Infogroup
Mardev-DM2 AL LOGIODICE MANAGER WEB CRM AND SONY STYLE 16745 W BERNARDO DR SAN DIEGO CA 92127-1907 858 942 8000
CUSTOMER SERVICE SYSTEMS
NetProspex Al Logiodice Manager Platform (310) 244-4000 10202 WASHINGTON BLVD CULVER CITY CA 90232-3119 (310) 244-4000 al.logiodice@am.sony.com
Development SonyStyle com
Sirista
Worldata Al Logiodice Platform Manager Sony Electronics, Inc. 16530 Via Esprillo San Diego CA 92127 (858) 942-2400 allogiodice@sony.com
ZoomInfo

11


Contact Record: Michael Spencer
Correct record Michael Spencer Director, Information Barclays Capital 745 Seventh Avenue New York NY 10019 (212) 412-2890 michael.spencer@barclayscapital.com
Technology
Data.com* Michael Spencer E2E Infrastructure Barclays Capital Inc. Unit 5 9 2 Churchill Place London E14 5RB +44.2071161000 michael.spencer@barclays.co.uk
Architect
D&B
Harte-Hanks
Infogroup
Mardev-DM2
NetProspex Michael Spencer E2E Infrastructure Barclays Capital Inc. 200 PARK AVE LOWR 3A NEW YORK NY 10166 (212) 412-4000 michael.spencer@barcap.com
Architect
Sirista MICHAEL SPENCER E2E INFRASTRUCTURE BARCLAYS CAPITAL 200 PARK AVE LOWR 3A NEW YORK NY 10166 2124124000 michael.spencer@barcap.com
ARCHITECT INC.
Worldata
ZoomInfo
* Jigsaw only accepts complete records. A member reported in 2009 that this contact is no longer with the company. We are also able to confirm that this is an undeliverable email. Hence this contact is in the Jigsaw Graveyard.

Contact Record: Dan Spiegel
Correct record Dan Spiegel Vice President of Engineering AdMarketplace 3 Park Avenue 27F New York NY 10016 631-219-6710 dspiegel@admarketplace.com
Data.com Dan Spiegel Vice President of Engineering adMarketplace 3 Park Ave Fl 27 New York NY 10016-5902 +1.212.925.2022 dan@admarketplace.com
D&B
Harte-Hanks
Infogroup
Mardev-DM2
NetProspex Dan Spiegel VP Engineering adMarketplace 3 Park Ave 27th Floor New York NY 10016 212-925-2022 dan@admarketplace.com
Sirista DAN SPIEGEL VP, ENGINEERING adMarketplace 3 Park Ave Fl 27 NEW YORK NY 10016 2129252022 dan@admarketplace.com
Worldata Dan Spiegel Vice President of Engineering adMarketplace 3 Park Avenue 27th Floor New York NY 10016 (212) 925-2022 dan@admarketplace.com
ZoomInfo

12


Observations about the data We also caution readers of this study against drawing conclusions about the
This study revealed several unexpected angles about tech data. For one, we capabilities of any particular vendor based on the comparative records of the
were surprised at how many IT professionals can be found at large enterprises. ten individuals. This is not a statistically projectable sample in any respect—
Paradoxically, we also notice that individuals in certain very large companies not only because it is too small, but also because these are simply ten people
may be relatively difficult to reach—judging from the large holes in several we happen to know and could persuade to lend their names. What we can
of the ten individual records. We hypothesize that some large enterprises conclude, however, is that important fields like direct phone number and email
might encourage their IT professionals to keep a low profile. address tend to be fluid in this vertical. And that the fast-moving tech industry
is characterized by high levels of turnover in jobs, skills and companies.
For another, the contact counts reported raise a critical issue for business
marketers. It’s apparent that IT titles are growing fuzzier over time. Consider The wide fluctuations in company counts and contact counts lead us to the
some of the titles used by our ten individuals: “Platform Manager,” “Reporting conclusion that no single vendor provides access to all the prospecting
Manager,” “Vice President of Engineering.” It’s well nigh impossible from companies and all the prospective contacts that marketers of technology
these titles to conclude that the person is in an IT role. Marketers may need may be looking to reach.
to broaden the variety of titles they specify to capture a wider set of targets. Advice to business marketers ordering from technology
Finally, the wide variation in company counts reported per SIC reminds us industry prospecting databases
that many vendors use proprietary industry categorization methodologies. Based on our conclusion that no single vendor is likely to give you access
This means that marketers need to be aware of the lack of standardization in to your entire target, our general recommendation about technology industry
determining how to classify any given company. This represents is a larger, vertical data is that you use multiple vendors to gain the breadth of market
ongoing problem in B-to-B database marketing, and an issue we will try to coverage you need.
address in a future study. Our specific guidelines for business marketers seeking to reach tech-buyer
As we expected, the data reported was fairly accurate, with only a few minor targets:
errors. When there were errors, they were not fatal for marketing purposes: n Given the wide variances in data quantity and quality, it’s essential that
The mail or email would still be deliverable, and the telephone call would you investigate thoroughly the data sources and maintenance practices of
eventually get to the prospect, in most cases. the vendors you are considering. In tech data particularly, quality trumps
Like earlier studies, the data field with the most problems—either missing quantity.
or less accurate than other data elements—was email address. n Specify exactly what you mean when ordering data. Don’t make any
assumptions that the vendor’s definition of a term is the same as yours.
When looking at the volume of complete records versus all contact records,
n Find out how your vendor gets at SIC, and whether they use some kind
keep in mind that vendors like Data.com, Stirista and NetProspex offer only
of conversion algorithm.
data that is complete by our definition.
n Ask your vendor for details on how they define and source title and job
function information, and how they are dealing with the new titles that
have come into use in recent years. Also inquire about when they update
their records, so you can get at the freshest data on this essential element.

13


n Conduct a comparative test before you buy. Here are three approaches Bernice Grossman is president of DMRS Group, Inc., a marketing database
you can try: consultancy in New York City. She is past chair of the B-to-B Council of
1. Send each potential vendor a sample of records from your house file The DMA. Reach her at bgrossman@dmrsgroup.com
and ask them to add data fields. Include a few dozen records on Ruth P. Stevens consults on customer acquisition & retention, and teaches
which you know the “truth,” to assess accuracy of what comes back. marketing at graduate schools and corporations. She is the author of
2. Order a sample of names with phone numbers from a prospective Maximizing Lead Generation: The Complete Guide for B2B Marketers,
vendor, and then verify the accuracy of the records by telephone. and Trade Show and Event Marketing. Reach her at ruth@ruthstevens.com.
3. Order 5,000 records from a single state, from multiple vendors.
Ask the vendors to deliver the file in ZIP sequence. Examine them. The authors gratefully acknowledge the valuable input of David Knutson,
A high incidence of identical records among the vendors will be a of Direct Business Systems.
strong indicator of likely accuracy. This publication is part of a series entitled Business-to-Business Database
We hope our research is useful to business marketers who are renting or Marketing, by Bernice Grossman and Ruth P. Stevens. Papers published to
buying data on technology buyers. This information will serve as a guide date include:
as you conduct your due diligence. “B-to-B Response Databases: A Comparative Analysis” (April 2011)
“Online Sources of B-to-B Data: A Comparative Analysis, 2010 Edition”
(March 2010)
“Online Sources of B-to-B Data: A Comparative Analysis” (January 2009)
“What B-to-B Marketers are REALLY Doing with Their Databases”
(September 2007)
“Enhancing Your B-to-B Database with Data Append” (November 2006)
“15 Thorny Data Problem That Vex B-to-B Marketers, and How to Solve
Them” (November 2006)
“Keep it Clean: Address Standardization Data Maintenance for Business Mar-
keters” (February 2006)
“Outsourcing Your Marketing Database: A ‘Request for Information’ is the
First Step” (March 2006)
“Our Data is a Mess! How to Clean Up Your Marketing Database” (October
2005)

These papers are available for download at
www.dmrsgroup.com and www.ruthstevens.com.

14

Business to Business Database Marketing

B-to-B Response Databases:
A Comparative Analysis
By Ruth P. Stevens and Bernice Grossman • April 2011

B-to-B Response Databases:
A Comparative Analysis
By Ruth P. Stevens and Bernice Grossman
April 2011

Executive Summary
As part of ongoing research on B-to-B data sources available to marketers, this white paper evaluates the volume
and accuracy of B-to-B marketing data provided by three response database suppliers. Like the results from our
analysis of compiled data sources, data coverage and accuracy varied widely among vendors. In fact, we were
surprised at how similarly the response databases behaved compared to the compiled databases studied in the
recent years. We continue to urge marketers who order response data to ask very carefully about the nature of
the data sources involved. We also strongly recommend that marketers conduct a pre-test of the data to assess
its applicability to their particular marketing need.

As a result of general enthusiasm about our past two We think it’s fair to say that all were intrigued by the
studies on the accuracy and completeness of the compiled opportunity and generally inclined to join. However, by
data available on B-to-B markets, we were asked to con- the time our deadline rolled around, only three vendors
duct similar research on the data found in the response were included. Why? For one thing, Infogroup decided
databases that have come on the scene in recent years. to make a single submission combining the records of
The timing of this suggestion was excellent, because the various response databases living under the Infogroup
response databases are maturing as a prospecting resource, umbrella (Direct Media’s Data Warehouse, and the Edith
and marketers are getting accustomed to sourcing names Roman databases). For another, several response database
from these pre-deduplicated amalgamations of response managers determined that only their list-owner clients
lists, often called “master files,” versus renting from a could make the decision to participate, and the complexity
batch of individual lists, as was the predominant list rental managing all those permissions was too great. Concerns
method in the past. were also expressed about competing on accuracy at the
contact level. One database manager explained to us, for
So, we invited as many managers of response databases example, that any given contact in his file could come
as we could find to participate in the study. Invited to from scores of list sources, each with its own degree of
participate were: accuracy, and all of which were maintained in the coop
 Direct Media’s Data Warehouse database.
 Edith Roman’s BRAD and BEN databases As a result, our study includes the following:
 IDG
 Mardev-DM2’s Decisionmaker database
 Infogroup
 MeritDirect and Experian’s b2bBase
 Mardev-DM2
 MeritDirect’s MeritBase
 Worldata
 Statlistics Our sincere thanks to them, and to everyone else who
 Worldata considered participating.

1

B-to-B Response Data: A Comparative Analysis

The growth of response databases As with our compiled data studies, we asked the vendors to
Business marketers have been the happy beneficiaries of provide company counts in a selection of critical industry
the rise of cooperative databases in the last decade. Some sectors, plus contact counts for specific companies, and
of these have been built by independent list management complete records on individual business people.
companies, who persuade their management clients to We specified the same ten industries as in the compiled
allow their lists to be added to the database, and rented that studies, and asked the vendors to tell us how many
way. Some cooperative databases have been built by own- companies they had in each of the ten, as indicated by
ers of multiple lists, such as large B-to-B trade publishers. SIC. For the contact data, we used the same set of well-
These databases offer many appealing features: known firms in each of the ten industries as were used
 Names from multiple list owners are collected, in the 2010 compiled data study.
de-duplicated, and in some cases
appended with additional firmo- Individual contacts in the study
graphic or behavioral data. Industry Name Company Title
 Marketers may select names based Retail Susan Sachatello Lands’ End Chief Marketing Officer
on useful variables like company Technology Theresa Kushner Cisco Systems Director, Customer Intelligence

size, title, and geography, across Not-for-profit Jim Siegel HealthCare Chaplaincy Director, Marketing and Communications
Optical equipment Stan Oskiera Edmund Optics, Inc. Vice President, Operations
all the lists, without worrying
Publishing Michael S. Hyatt Thomas Nelson President and Chief Executive Officer
about individual minimum list
Legal services John E. Tobin, Jr. New Hampshire Legal Executive Director
order quantities. Assistance
 List owners are paid by usage on Healthcare Brian A. Nester Lehigh Valley Health Senior Vice President, Physician Hospital
a name-by-name basis. Since list Network Network Development
Education Russell Winer New York University William Joyce Professor of Marketing;
purchase is easier for marketers, Stern School of Business Chair, Department of Marketing
in theory, owners’ list revenues Tech services Dale Mesnick Smart Solutions, Inc. Treasurer
are higher than they could get by Industrial Bill Bullock Turbosteam General Manager
limiting rentals to the traditional
list-by-list basis.
 Since records come from multiple sources, they may We also recruited ten new business people in a variety of
tend to be more accurate than single-sourced data. industries and in various job categories to agree to serve
as this year’s guinea pigs. We are grateful to these brave
A note about private cooperative databases
souls for their generous support of this study.
While the MeritBase and the Mardev-DM2 Decisionmaker
are prominent examples of coop databases, another type We asked only one qualitative question, inviting the
of cooperative database is also available today, this one vendors to explain their competitive positioning in the
private and available only to members. A leading example marketplace.
is Abacus’s B2B Alliance. Only list owners who join the The positioning statements
Abacus coop and put their names in may take names out Here is how the vendors described themselves in response
of the database. The identity of member companies is kept to the following question:
confidential. Because of the inaccessibility to non-member Provide a statement of no more than 150 words that
marketers, we did not ask Abacus or other similar private describes your online B-to-B data product/service,
database cooperatives to participate in the study. including how you are positioned, meaning your
The scope and intent of the study competitive differentiation. In short, this question is,
We followed the same approach as our recent research “Who are you and how are you different?”
on compiled databases, to get answers to the concerns of Infogroup
business marketers about data volume, completeness and Infogroup is the leading provider of data and interactive
accuracy. By using a similar research methodology, we resources that enable targeted sales, effective marketing
also hoped to provide some apples-to-apples comparison and insightful research solutions. Among Infogroups assets
between the contents of response databases and compiled are powerful B-to-B response driven databases. These
assets allow access to over 100 million key decision-makers
databases.

2


penetrating virtually every business site in the US and The company counts reported
Canada. They contain over 25 million executive email Here are the company counts in each of the ten industries
addresses, and enable users to choose from over 48 buying
reported by the vendors in response to the question:
influence selectors including multi-buyers, job function,
industry, products purchased, and more. Infogroups assets State the number of U.S. firms you have on your file
have over 1,500 “list specific” response-generated data within each of these 10 SICs.
sources and are enhanced with firmographic and trans- Infogroup Mardev-DM2 Worldata
actional data elements for targeted campaigns based on 32 Stone, clay and glass products 43,318 82,416 20,571
our expert strategic guidance. With over 100 million buyers 56 Apparel and accessory stores 297,473 15,319 18,137
and 32 million buying sites, our solutions are designed to 28 Chemical and allied products 54,807 224,308 62,210
maximize ROI for our customers. They are sourced from 64 Insurance agents, brokers & services 365,758 1,082,065 72,267
responder lists of mail order catalogers, publishers, book 73 Business services 3,190,830 894,257 84,703
buyers, seminar and conference attendees and association
81 Legal service 546,267 892,825 123,712
memberships.
80 Health services 2,059,979 329,153 1,315,999
82 Educational service 524,256 450,560 657,129
Mardev-DM2
35 Machinery, except electrical 152,375 405,674 206,547
For B2B marketers who need to expand their domestic or
48 Communications 203,792 173,422 192,266
worldwide market footprint or accelerate their sales, Mardev-
DM2 delivers a targeted audience of buyers and the global Do you code firms with NAICS? (Y/N) Y Y Y
marketing services that most effectively reaches them. Unlike
companies who provide compiled or standard company data, The contact counts reported
Mardev-DM2 delivers a level of detail within our data that en-
Here are the counts for contacts at each of ten well-known
ables better targeting at the individual level and far surpasses
the quality of most data providers. In addition, Mardev-DM2 companies, in response to the question: Provide the total
takes a consultative, creative and objective-based approach number of contacts you have at each firm, U.S. only,
to each new client project, whether for B2B postal, email or including headquarters and all branch locations.
telemarketing data, lead generation and nurturing programs,
Infogroup Mardev-DM2 Worldata
or fully integrated strategic marketing services. We meet
Andersen Windows 330 107 0
each client where they are and work with them to develop
a complete marketing program – from planning to execution Nordstroms 349 5 531
to measurement – to ensure the best ROMI and overall Monsanto 6,527 1,679 1,288
success. A few of our core industries include: IT/Computers, MetLife 12,073 11,625 1,722
Building/Construction, Manufacturing, Insurance/Finance, Accenture 34,355 6,803 472
Engineering, Electronics, Legal, HR/Training, Foodservice/ Baker & McKenzie 2,128 1,082 320
Hospitality. Methodist Hospital System 1,010 767 201
ETS (Educational Testing Service) 2,333 515 89
Worldata Dell 7,060 8,872 1,446
Worldata is the leading data agency and list brokerage/ Verizon 30,684 18,353 2,938
management firm in the U.S. Our ability to source, negotiate
and utilize the latest technologies gives us a competitive
advantage over the general list rental buying marketplace. Here are the figures on complete counts for each industry,
Our primary focus is with the Email, Direct Mail and Tele- in response to the question: The number of “complete”
marketing categories. We help marketers to execute contact records you have at each firm. Complete means
prospect marketing programs, data hygiene initiatives and including full name, address, title, phone, fax and email.
overall direct marketing strategies. More than 800 customers
worldwide from all types of businesses and organizations— Infogroup Mardev-DM2 Worldata
from enterprise technology, publishing, and online education Andersen Windows 158 41 0
to business services, nonprofits, and associations—use Nordstroms 284 2 331
Worldata to leverage data assets, procure key datasets Monsanto 340 880 988
and find overall solutions to customer and prospect data MetLife 1,468 1,965 1,472
initiatives. Accenture 5,660 1,048 302
Baker & McKenzie 1,779 430 237
Methodist Hospital System 321 224 176
ETS (Educational Testing Service) 318 213 66
Dell 852 2,991 1,099
Verizon 1,937 3,881 2,019

3


The contact records reported
Here are the records for our ten individual business people, in response to the following directions.
Please pull the record of each of these persons as it currently appears on your file. Submit the record in its entirety.
Note: Please do not use any other data sources (e.g., tele-verification, or Internet search) to research these names.
We have secured permission from these 10 people to include their data in this research, and we have told them they
will not be contacted or researched in any way by the participating suppliers.

Contact: Susan Sachatello

Correct data Infogroup Mardev-DM2 Worldata
First name Susan Susan SUSAN
Last name Sachatello Sachatello SACHATELLO
Title Chief Marketing Officer Senior Vice President Marketing SR VICE PRESIDENT MARKETING
Company Lands' End Lands' End, Inc. LANDS' END, INC.
Address 1 5 Lands' End Lane 1 Lands End Ln LANDS END LN
Address 2
City Dodgeville Dodgeville DODGEVILLE
State WI WI WI
Zip 53595 53595 53595-0001
Office phone 608-935-4169 608-935-9341 608 935 9341
Email susan.sachatello@andsend.com susan.sachatello@landsend.com SUSAN.SACHATELLO@LANDSEND.COM

Contact: Theresa Kushner

First name Theresa Theresa THERESA Theresa
Last name Kushner Kushner KUSHNER Kushner
Title Director, Customer Intelligence Director of Customer Intelligence DIRECTOR DIRECTOR, CUSTOMER INTELLIGENCE
Company Cisco Systems Cisco Systems, Inc. CISCO SYSTEMS INC Cisco Systems, Inc.
Address 1 170 West Tasman Drive 170 W Tasman Dr BLDG 8 170 W TASMAN DR 170 W Tasman Dr
Address 2 SJ08-3 SJ08-3
City San Jose San Jose SAN JOSE San Jose
State CA CA CA CA
Zip 95134-1706 95134 95134-1700 95134-1706
Office phone 408-526-8774 408-526-8774 (408) 526-8774 408-526-8774
Email thkushne@cisco.com thkushne@cisco.com THKUSHNE@CISCO.COM thkushne@cisco.com

Contact: Jim Siegel

First name Jim Jim JIM
Last name Siegel Siegel SIEGEL
Title Director, Marketing and Director Marketing & Communication DIRECTOR OF MARKETING AND
Communications COMMUNICATIONS
Company Healthcare Chaplaincy The Healthcare Chaplaincy Inc. THE HEALTHCARE CHAPLAINCY INC
Address 1 315 East 62nd Street 315 E 62nd St FL 4 307 EAST 60TH STREET
Address 2 4th Floor
City New York New York NEW YORK
State NY NY NY
Zip 10065-7767 10065 10022-1505
Office Phone 212-644-1111 x141 212-644-1111 212-644-1111 ext. 141
Email jsiegel@healthcarechaplaincy.org jsiegel@healthcarechaplaincy.org jsiegel@healthcarechaplaincy.org

4


Contact: Michael S. Hyatt

First name Michael S. Michael S MICHAEL MICHAEL
Last name Hyatt Hyatt HYATT HYATT
Title President and Chief Executive President, Chief Executive Officer CHIEF INFORMATION OFFICER PRESIDENT AND CHIEF EXECUTIVE
Officer OFFICER
Company Thomas Nelson Thomas Nelson Inc THOMAS NELSON, INC. THOMAS NELSON INC.
Address 1 P.O. Box 141000 501 Nelson Pl 141000 PO BOX 501 NELSON PL
Address 2 PO Box 141000 501 NELSON PL NASHVILLE
City Nashville Nashville NASHVILLE NASHVILLE
State TN TN TN TN
Zip 37214 37214 37214-3600 37214-3600
Office Phone 615.902.1100 615-889-9000 615 889 9000 615-902-1100
Email mhyatt@thomasnelson.com mhyatt@thomasnelson.com MHYATT@THOMASNELSON.COM

Contact: Stan Oskiera

First name Stan Stan STAN Stanley
Last name Oskiera Oskiera OSKIERA Oskiera
Title Vice President, Operations Vice President of Operations VP OPERATIONS VICE PRESIDENT OPERATIONS
Company Edmund Optics, Inc. Edmund Optics, Inc. EDMUND OPTICS INC Edmund Optics
Address 1 101 E. Gloucester Pike 101 East Gloucester Pike 101 E GLOUCESTER PIKE 101 E. Gloucester Pike
Address 2
City Barrington Barrington BARRINGTON Barrington
State NJ NJ NJ NJ
Zip 08007 08007 08007-1331 08007
Office Phone 856-547-3488 ext. 6887 856-547-3488 8565473488 856-547-3488
Email soskiera@edmundoptics.com soskiera@edmundoptics.com soskiera@edmundoptics.com

Contact: John E. Tobin, Jr.

First name John E. John JOHN JOHN
Last name Tobin, Jr. Tobin TOBIN TOBIN
Title Executive Director Executive Director EXECUTIVE DIRECTOR EXECUTIVE DIRECTOR
Company New Hampshire Legal Assistance New Hampshire Legal Assistance NEW HAMPSHIRE LEGAL ASSISTANCE NEW HAMPSHIRE LEGAL ASSISTANCE
Address 1 117 North State St., 117 N State St 3117 N STATE 117 North State St.
Address 2
City Concord Concord CONCORD Concord
State NH NH NH NH
Zip 03301 03301 03301 03301
Office Phone 603-224-4107 x 2816 603-223-9750 603 668 2900 603-224-4107 ext.2816
Email jtobin@nhla.org jtobin@nhla.org JTOBIN@NHLA.ORG JTOBIN@NHLA.ORG

Contact: Brian A. Nester

First name Brian A. Brian BRIAN
Last name Nester Nester NESTER
Title Senior Vice President Doctor of Osteopathy SVP PHYSICIAN PRACTICE
Company Lehigh Valley Health Network Lehigh Valley Health Network LEHIGH VALLEY HOSPITAL EMERGENCY
Address 1 Cedar Crest and I-78, PO Box 689 240 S CEDAR CREST BLVD & I-78
Address 2 P. O. Box 689 EMERGENCY MEDICINE
City Allentown Allentown ALLENTOWN
State PA PA PA
Zip 18105 18105 18105
Office Phone 610-402-7544 610-402-8111 610-402-8111
Email Brian.Nester@lvhn.org brian.nester@healthnetworklabs.com Brain.Nester@LVH.COM

5


Contact: Russell Winer

First name Russell Russell Russell
Last name Winer Winer Winer
Title William Joyce Professor of Marketing; Professor; Chair Marketing Chair, Marketing Department
Chair, Department of Marketing
Company Stern School of Business NYU-Stern School Of Business NEW YORK UNIVERSITY
Address 1 40 West 4th Street 40 W 4th St 44 West Fourth Street
Address 2 Tisch Hall 806 Tisch Hall Marketing Dept Stern School of Business
City New York New York New York
State NY NY NY
Zip 10012-11 10012 10012
Office Phone 212.998.0540 212-998-0100 212-998-0540
Email rwiner@stern.nyu.edu rwiner@stern.nyu.edu

Contact: Dale Mesnick

First name Dale Dale DALE DALE
Last name Mesnick Mesnick MESNICK MESNICK
Title Treasurer Senior Manager; Finance Executive VICE PRESIDENT TREASURER
Company Smart Solutions, Inc. Smart Solutions, Inc. SMART SOLUTIONS INC SMART SOLUTIONS INC
Address1 23900 Mercantile Road 23900 Mercantile Rd 23900 MERCANTILE RD 23900 MERCANTILE RD
Address2
City Cleveland Cleveland CLEVELAND CLEVELAND
State OH OH OH OH
ZIP 44132 44122 44122-5910 44122-5910
Office phone (216) 765-1122, ext. 8227 216-765-1122 216 765 1122 2167651122
Email dmesnick@smartsolutionsonline.com dmesnick@smartsolutionsonline.com dmesnick@smartsolutionsonline.com

Contact: Bill Bullock

First name Bill William BILL WILLIAM
Last name Bullock Bullock BULLOCK BULLOCK
Title General Manager General Manager GENERAL MANAGER GENERAL MANAGER
Company Turbosteam Turbosteam LLC TURBOSTEAM CORP TURBOSTEAM CORPORATION
Address1 161 Industrial Blvd 161 Industrial Blvd 161 INDUSTRIAL BLVD 161 INDUSTRIAL BOULEVARD
Address2
City Turners Falls Turners Falls TURNERS FALLS TURNERS FALLS
State MA MA MA MA
ZIP 01376 01376 01376-1611 01376-1611
Office phone (413) 676-3016 413-863-3500 413 863 3500 413-863-3500
Email Bbullock@turbosteam.com bbullock@turbosteam.com WBULLOCK@TURBOSTEAM.COM WBULLOCK@TURBOSTEAM.COM

Observations about the data Just as we were surprised at the results of our compiled
Having done two successive annual studies on the accu- data studies, which showed better than expected accuracy,
racy and completeness of B-to-B compiled data, we we are now surprised at the response data we looked at,
brought with us certain assumptions as we prepared for a which is broader than we anticipated. The number of
study on response data. Most direct marketers expect that, companies reported by SIC, and the number of contacts
while compiled data provides better market coverage but is per company, were impressive. Comparing the counts
less accurate, response data is more accurate but gives you with last year’s compiled data (which is not quite fair,
less breadth of coverage. since a lot can happen in B-to-B data in one year) we

6


would say the response databases are holding their 2. Order a sample of names with phone numbers from
own, certainly debunking our long-held assumption that a prospective vendor, and then verify the accuracy
response files give limited market coverage. When it of the records by telephone.
comes to the individual contacts, less than a handful 3. Order 5,000 records from a single state, from multi-
were missing records or particular data elements. ple vendors. Ask the vendors to deliver the file in
As we expected, the data reported was fairly accurate, ZIP sequence. Examine them. A high incidence of
with only a few minor errors. When there were errors, identical records among the vendors will be a strong
they were not fatal for marketing purposes: The mail or indicator of likely accuracy.
email would still be deliverable, and the telephone call We hope our research is useful to business marketers who
would eventually get to the prospect, in most cases. are renting or buying response data. This information will
The data field with the most problems—either missing serve as a guide as you conduct your due diligence.
or less accurate than other data elements—was email.
We generally conclude that: Ruth P. Stevens consults on customer acquisition &
 The data available in response databases is quite retention, and teaches marketing to graduate students
similar in accuracy and completeness to compiled data. at Columbia Business School. She is the author of
 As was shown by our past studies, data varies by vendor, Trade Show and Event Marketing and the forthcoming
and each vendor has its strengths and weaknesses. Maximizing Lead Generation. She can be reached at
ruth@ruthstevens.com.
Advice to business marketers ordering
Bernice Grossman is president of DMRS Group, Inc.,
from response databases
Our advice to marketers about response data is similar to a marketing database consultancy in New York City.
that on compiled data. We urge caution when ordering She is past chair of the B-to-B Council of The DMA.
data from these databases. Marketers should develop a She can be reached at bgrossman@dmrsgroup.com.
detailed ordering methodology, to increase the likelihood
that the data they receive is what they were seeking. The authors are grateful to Denise Moser of Mardev-DM2
for suggesting that this study be undertaken.
Our guidelines:
This publication is part of a series entitled Business-to-
 Given the wide variances in data quantity and quality, Business Database Marketing, by Bernice Grossman
it’s essential that you investigate thoroughly the data and Ruth P. Stevens. Papers published to date include:
sources and maintenance practices of the vendors you
are considering. “Online Sources of B-to-B Data: A Comparative Analysis,
 Specify exactly what you mean when ordering data. 2010 Edition” (March 2010)
Don’t make any assumptions that the vendor’s “Online Sources of B-to-B Data: A Comparative Analysis”
definition of a term is the same as yours. (January 2009)
 Be very specific about industry selections. Find out “Our Data is a Mess! How to Clean Up Your Marketing
if the vendor uses SIC, or some kind of conversion Database” (October 2005)
algorithm. “Keep it Clean: Address Standardization Data Mainte-
 Keep an eye out for vendor specialization by industry. nance for Business Marketers” (February 2006)
Companies and contacts vary widely by vendor. For “Outsourcing Your Marketing Database: A ‘Request for
additional market coverage we suggest that you explore Information’ is the First Step” (March 2006)
industry specialty files for both prospecting and data “Enhancing Your B-to-B Database with Data Append”
append purposes. (November 2006)
 Conduct a comparative test before you buy. Here are “15 Thorny Data Problem That Vex B-to-B Marketers,
three approaches you can try: and How to Solve Them” (November 2006)
1. Send each potential vendor a sample of records “What B-to-B Marketers are REALLY Doing with
from your house file and ask them to add data fields. Their Databases” (September 2007)
Include a few dozen records on which you know These papers are available for download at
the “truth,” to assess accuracy of what comes back. www.dmrsgroup.com and www.ruthstevens.com.

7

1910 South Highland Avenue, Suite 103 Lombard, IL 60148-6129 www.wheatongroup.com

Jim Wheaton is a Co-Founder and Principal at Wheaton Group
(www.wheatongroup.com), a Chicago-area company that specializes in direct
marketing consulting and data mining, data quality assessment and assurance, and
the delivery of cost-effective marketing databases. The firm also offers clients the
smartFOCUS suite of desktop access and campaign management software.

Jim has been a database marketing professional since 1981. He has held positions
ranging from building and managing a cutting-edge data mining and strategic
consulting practice, to profit and loss responsibility for several major direct
marketing product lines.

Previous to co-founding Wheaton Group in January 2000, Jim was Senior Vice
President of Strategic Consulting at KnowledgeBase Marketing. There, he
combined his deep expertise in data warehousing and processing, quantitative
analysis, demographic overlay information, and “hands on” direct marketing
management to create innovative, data-driven solutions for KnowledgeBase
clients. Also, he was a named an Officer at Kestnbaum & Company upon that
firm’s acquisition by KnowledgeBase.

Prior to KnowledgeBase, Jim was Vice President of Research & Consulting for
Wiland Services/Neodata. There, he built a team of statisticians and database
marketers that specialized in data mining and strategic consulting. In addition, he
headed up the firm’s Value Added Reseller business, encompassing the RL Polk
(now, Equifax) suite of demographic overlay data products.

Previously, Jim was a database marketing consultant, first with Kestnbaum and
then with Wiland. Even earlier, he was a line manager at MBI, Inc., one of the
world’s largest direct marketers of collectibles, where he had profit and loss
responsibility for several continuity and subscription lines of businesses.

Jim has authored over 200 industry articles and speeches, is former Chairman of
The DMA Analytics Council, and holds an M.B.A. from The University of
Chicago and a B.A. from Brown University.

9/18/2012

Deadly Sins and the Ten Commandments:

How to Achieve Best-Practices
Database Content
and Key Metrics Reporting
Jim Wheaton
Principal, Wheaton Group
919-969-8859, jim.wheaton@wheatongroup.com
www.wheatongroup.com

1

Overview of Wheaton Group

• We provide the link between the data and the marketing.
– Database construction, management and hosting.
– Data mining and consulting, including metrics and reporting.
– Collaborate on multi-channel communication programs.
– Total focus on data quality assessment and assurance.

• For example, outsourced database marketing department for
Godiva Chocolatier, Excelligence Learning Corporation, and
White Cap Construction Supply.

• Four Principals with over 120 years of experience across
over 100 clients, and many verticals.

• A focus on B2B through our B2BMarketing.com joint venture.

2

1

9/18/2012

Overview of Today’s Session

• Best-Practices Marketing Database Content, the foundation for:
– Analysis and measurement.
– Data-driven CRM.

• The First 5 Commandments of Best-Practices Content.

• Insightful Key Performance Indicators (“KPIs” and “Dashboards”).

• The Second 5 Commandments of Best-Practices
Content.

3

The CRM Revolution:
“Star Wars” Database & Business Intelligence Technologies

• Access and manipulate massive amounts of data
in seconds.

• Powerful GUI interfaces for eye-catching
dashboards and reports.

• However, a caveat…

4

2

9/18/2012

Car Restorations and Best-Practices Content:
Some Similarities

• “GI” doesn’t necessarily mean “GO.”

• It’s all about hard, ugly work and attention to detail:
– Data audits and other forms of quality assurance.
– Capturing a business in the data, dashboards
and reports.

• Bad content always costs you money!
– An example from financial services…

• Without all the hard work, result will be “all show
and no go.”

5

Best-Practices Marketing Database Content:
Foundation for Analysis, Measurement & Data-Driven CRM!

• Some key attributes:
– Properly-linked customer hierarchies.
– All customer-to-company contacts.
– All company-to-customer contacts.

• Rapid creation of multiple past-point-in-time (“time-0”) views.
– Predictive analytics such as modeling.
– Cohort analysis such as lifetime value estimations.
– Monitor changes in customer inventories (KPIs).
– Unanticipated back-in-time reporting capability,
such as: how to catch a serial killer.

6

3

9/18/2012

Commandment #1:
Customer Hierarchies Must Be Created & Maintained

• Requires:
– Robust linkages across multiple database levels.
– Scrupulous application of consolidation procedures.

• Supports, for example:
– Insight into the true nature of multi-buyers.
– Accurate performance metrics such as lifetime value.
– Innovative targeting programs.

• For example, selling to law enforcement
agencies…

7

Commandment #2:
Inquiry & Demand Transactions Must Be Maintained

• Examples of demand transactions:
– Retail and direct including e-commerce: orders and items.
– Subscriptions and continuities: payments.

• Include bill-to/ship-to linkages.
– For B2B, universal applicability.
– For B2B and B2C, seminal to gifting.

8

4

9/18/2012

Commandment #3:
Post-Demand Transactions Must Be Maintained

• Track progression from Demand to Gross to Net, including:
– Backorders and cancels.
– Returns, refunds and rebates.
– Exchanges and allowances.
– Delivery issues.

• Critical for large differences between Gross and Net, such as:
– Trial periods at reduced or no cost.
– Bad debt.
– High-return businesses such as women’s apparel.

• Improves predictions and customers needing
remedial action.

9

Commandment #4:
Promotion Transactions Must Be Maintained

• Maintain all promotional contacts across all channels.
– Do not forget email.
– Field sales and phone “touches,” if you can get them.

• Typical content:
– Start date.
– End date.
– Coding (source codes, key codes, offer codes, etc.).
– Offer terms (buy-one-get-one, percentage-off, dollars-off, etc.)

• If you are building a database, include the old promotions!

• Example of a 7-figure system with no promotion history…

10

5

9/18/2012

Commandment #5:
Supplemental Sources Must Be Considered

• For example:
– Overlay demographics and psychographics.
– For B2B, overlay “firmagraphics.”
– Customer service (complaints, etc.).
– Customer-generated gift messages.

• New media inputs (e.g., social networks and
complainers).

11

Key Performance Indicators:
The Four Rules

• Rule #1: Strive for simplicity.

• Rule #2: Customer inventory report as the foundational KPI.

• Rule #3: Customize the customer inventory report.

• Rule #4: Supplement with “The Why KPIs.”

12

6

9/18/2012

Key Performance Indicator:
The Customer Inventory Report

• Three factors determine monthly gross revenue (demand):
– Number of customers.
– Percent monthly buying rate.
– Demand per buying customer.

• Track monthly, including year-over-year.
– Or, as appropriate, weekly, seasonal, etc.
– For example…

13

Supplement with “The Why KPIs”

• Include net revenue if have significant post-demand activity.

• Include potential “why” factors, as appropriate, such as:
– Backorder/cancel rates.
– Out-of-stocks.
– Returns/exchange rates.
– Order-to-shipment turnaround.
– Complaint levels.
– Circulation variations.
– Product changes.
• For example…

14

7

9/18/2012

The Ten Commandments of
Best-Practices Marketing Database Content

• #1: Customer hierarchies must be created and maintained.

• #2: Inquiry and demand transactions must be maintained.

• #3: Post-demand transactions must be maintained.

• #4: Promotion transactions must be maintained.

• #5: Supplemental sources must be considered.

• And now, for Commandments 6 through 10…

15

Commandment #6:
Data Semantics Must Be Complete, Consistent & Accurate

• Semantics = naming conventions & coding/classification schemes.
– Beware of changes, and of different coding across divisions.

• A common problem area is merchandise classification.
– For example, class-department-division-season combinations.
– Often reworked, but often not historically.

• If one does not exist, then invent one!

• Add a customer point-of-view.
– For example, a merchandise segmentation we did…

16

8

9/18/2012

Commandment #7:
The Data Must Not Be Archived or Deleted

• Rolling off older data is a common phenomenon.
– Ironic because, finally, disk space is cheap.

• For example, models built off 36 months of data…

17

Commandment #8:
The Data Must Be Maintained at the Atomic Level

• Can always aggregate, but can never disaggregate.

• For example, thanks to atomic-level data being maintained,
the serial killer was caught.

18

9

9/18/2012

Commandment #9:
The Data Must Be Time-Stamped

• Re-creation requires going beyond the naturally-date-driven.
– Address changes, progression of change statuses,
demographics, etc.

• Modeling and product progression analysis.

• “The Easter Monster” & other floating events that drive behavior.
– The importance of relative analysis.
– For example, the unnecessary fire-drill…

• The serial killer was caught, but what if:
– Only the most current address had been saved?
– The old addresses had not been date-stamped?

19

Commandment #10:
The Data Must Not Be Overwritten

• After the financial services example, enough said!

• Do not confuse with full-replacement update techniques,
when the incremental approach is not feasible.

20

10

9/18/2012

The Ten Commandments of
Best-Practices Marketing Database Content

• Customer hierarchies must be created and maintained.
• Inquiry and demand transactions must be maintained.
• Post-demand transactions must be maintained.
• Promotion transactions must be maintained.
• Supplemental sources must be considered.
• Data semantics must be complete, consistent and accurate.
• The data must not be archived or deleted.
• The data must be maintained at the atomic level.
• The data must be time-stamped.
• The data must not be overwritten.

21

For Additional Information

• “Marketing Should Control the Marketing Database, Not IT,” Chief
Marketer, April 15, 2011

• “True Marketing Databases Make Sophisticated Data Mining
Possible,” Direct Newsline, August 19, 2010

• “How Marketing Databases Differ from Operational Databases,”
Direct Newsline, June 29, 2010

• “The First Five Commandments of Database Content Management,”
Multichannel Merchant, February 1, 2007

• “The Second Five Commandments of Database Content
Management,” Multichannel Merchant, May 1, 2007

22

11

9/18/2012

Deadly Sins and the Ten Commandments:

How to Achieve Best-Practices
Database Content
and Key Metrics Reporting
Jim Wheaton
919-969-8859, jim.wheaton@wheatongroup.com
www.wheatongroup.com

23

12

Introduction to Wheaton Group

Wheaton Group LLC, launched in 1989 as “Strategic Insight” and renamed in January 2000, is a
direct and database marketing services firm led by four Principals with over 120 years of
experience across well over 100 clients and spanning:
Business-to-business, business-to-consumer and B2B/B2C hybrids.
Many vertical industries including catalog, consumer package goods, financial services,
non-profit, publishing and telecommunications.
All major selling and distribution channels including retail, direct (mail, phone and e-
commerce) and field sales.

Wheaton Group’s work is grounded in a continuous focus on data quality assessment and
assurance. The firm’s core competencies include:
The creation of marketing databases that offer the best-practices content required to
support the most advanced forms of analytics, and hosted and maintained either by us
or the client.
Robust data management services including the execution of selects for multi-channel
promotional campaigns.
The leveraging of marketing database content through advanced analytics, reporting and
quantitatively-grounded consulting.

Wheaton Group also provides its services through the B2BMarketing.com joint venture.

Biographies of Wheaton Group’s Four Principals
Jim Wheaton has been a direct and database marketer since 1981. He began in line
management. Then, he was a consultant at Kestnbaum & Company, Vice President of
Research & Consulting at Wiland Services, Senior Vice President of Strategic Consulting at
KnowledgeBase Marketing, and Co-Founder of Wheaton Group. Jim has authored well over
200 articles and speeches, is former Chairman of The DMA Analytics Council, and holds an
MBA from The University of Chicago and a BA from Brown University.

Cynthia Wheaton has been a direct and database marketer since 1978. She began in line
management, spearheading new venture development at Sara Lee Direct and then at World
Book Encyclopedia. One such venture was “Just My Size,” the national retail brand. Cynthia
later served as VP of Marketing for GRI Corp. She became a consultant in 1986 at Kestnbaum
& Company. In 1989, she launched Strategic Insight, the precursor to Wheaton Group. Cynthia
has an MBA from the University of North Carolina at Chapel Hill as well as a BA in English.

Boris Gendelev has specialized in marketing data warehousing, software development and
analytics since joining the direct and database marketing consulting profession in 1983. He
began at Foote Cone & Belding Direct Marketing Systems. Then, Boris was a Vice President at
Precision Marketing, a position that he maintained throughout the Direct Marketing Technology
(“Direct Tech”) and Experian acquisitions. Boris joined Wheaton Group in 2002 as a Principal.
He has an MBA from The University of Chicago as well as a BS in Computer Science.

Leo Sterk has specialized in strategic analytics since joining the direct and database marketing
consulting profession in 1984. He began in the industry as a consultant at Kestnbaum &
Company. Then, Leo was a Vice President at Precision Marketing, a position that he
maintained throughout the Direct Tech and Experian acquisitions. Leo joined Wheaton Group in
2004 as a Principal. He has an MBA from The University of Chicago, and bachelors and
masters degrees from the University of Illinois-Urbana in the field of urban planning.

For more information, contact Jim Wheaton (919-969-8859; jim.wheaton@wheatongroup.com).


Why Marketing Should Control the Marketing Database, Not IT
By Jim Wheaton

Original version of an article that appeared in the April 15, 2011 issue of “Chief Marketer”

I have been a direct and database marketing consultant since 1984. In all that time, one consistent
verity is that most internal IT departments think they can – and should – be responsible for the
marketing database. In many instances, the IT department has no idea what it is talking about.

Why is this? I think it has to do with the term “marketing database.” IT professionals hear the word
“database,” and say, “Ah ha! That means a system, and systems are in my bailiwick.”

Well, the IT guys are partly right, but they are mostly wrong. This is because, for the majority of
direct marketers, the systems component of a marketing database is relatively trivial. Sure, there are
some multiple-terabyte systems with near-real time update cycles, and dozens of users who need
simultaneous access. But, most databases are much smaller, and with no more than one of two users
accessing it at any given point in time.

For these smaller applications, the real challenge lies with the content; that is, the “stuff” of which
the database is constituted. This “stuff” can be very difficult to render consistent and usable because
of three challenges, none of which lies within the bailiwick of an IT professional:

Challenge #1: Name and Address Processing

B2C account information must be aggregated to the individual and household levels. Likewise, B2B
account information must be aggregated to the individual, site and organizational levels. These
multiple levels of customer (and, when applicable, prospect) definition are required to:
Perform accurate analysis, scoring, promotional selections, and response attribution.
Properly allocate marketing-spend to each customer.

In order to pull all of this off:

First, address standardization, ZIP Code correction, parsing and unduplication technologies – guided
by carefully-constructed business rules – must be employed to match accounts on a combination of
names, company names, addresses, phone numbers, and – when applicable – bill-to/ship-to
relationships.

Then, the matches must be unified into a single non-circular cross-reference that:
Assigns each account to one and only one individual.
Assigns each individual to one and only one B2C household or B2B site.
For B2B, assigns each site to one and only one organizational entity.

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Finally, all of this must be maintained over time so that it is easy to make adjustments and
enhancements, and re-consolidate the data, per ongoing quality assurance that is conducted on the
matches.

Challenge #2: Transaction Processing

Hopefully, your customers are doing lots of buying. Most likely, the purchases are taking place
across multiple channels. You almost certainly have at least one e-commerce site, and you probably
have an in-bound call center. If some of your revenue comes from B2B, then you are likely to have
an outbound sales team and/or field sales force.

The data from each of these sources will have its own structure and anomalies. Multiple divisions
often mean even more permutations of data structures and anomalies, especially when company
mergers have taken place.

The bottom line is that transactional data is not particularly usable in its raw format. In order to
make the data usable, the semantics must be rendered historically complete, consistent and accurate,
and correspond with core business concepts. Also, the data must be time-stamped and maintained
down to the atomic level, and must not be overwritten, archived or deleted. Finally, the following
must be included:
Demand, as opposed to “shipped” or “completed,” transactions.
Promotion transactions, even for those that did not result in a purchase.
The following, when applicable: inquiry and post-demand transactions, and supplemental
sources such as demographics, “firmographics” and social networks.

Challenge #3: The Creation of Past-Point-In-Time Views

A modern database must support – on-demand – any calculation, aggregation or subset that logically
can be generated from the underlying data. This requires a mechanism to allow the efficient and
rapid re-creation of multiple past-point-in-time (“time-zero” or “time-0”) views. Time-0 views are
necessary because all of the dimensions to be analyzed cannot be known and "frozen" in advance.
These views form the basis for virtually all meaningful analytics, by allowing customers to be
classified based on detailed histories only up to the appropriate past-points-in-time.

Cohort analysis such as lifetime value is an important example of data mining that depends on the re-
creation of multiple time-0 views. Likewise, the analysis and validation files required for predictive
models are based on time-0 views.

Another application of cohort analysis is the monitoring of changes in customer “inventories,” such
as fluctuations in segment sizes and performance over time. Still another is the analysis of historical
trends within subsets of promotional channels, products and services offered, etc.

Final Thoughts

How many internal IT departments have the chops to handle these three challenges of marketing
database content? Not many! Those that do are typically concentrated among companies in which
the scale of the application is such that it makes sense to hire a team of experienced professionals.

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Things are different for smaller database applications in which it is not cost effective to hire multiple
experienced professionals, much less staff to the level of job-function redundancy required to
counteract the inevitable resignations and terminations. All of this, to return to my opening
statement, is why most IT departments have no idea what they talking about when they think they
can – and should – be responsible for the marketing database.

Jim Wheaton is a Principal at Wheaton Group (www.wheatongroup.com), and can be reached at
919-969-8859 or jim.wheaton@wheatongroup.com. The firm specializes in direct marketing
consulting and data mining, data quality assessment and assurance, and the delivery of cost-effective
marketing databases

3


True Marketing Databases Make Sophisticated Data Mining Possible
By Jim Wheaton

Original version of an article that appeared in the August 16, 2010 issue of “Direct Listline”

(This topic was first covered in the June 18, 2010 Direct Listline article, “How Marketing Databases
Differ from Operational Databases.”)

There is a big difference between a Marketing Database and an Operational Database. A Marketing
Database supports sophisticated data mining and an Operational Database does not.

Sophisticated data mining, in turn, is impossible without the ability to recreate multiple past-point-in-
time (“time 0”) views. This is because data mining professionals work in the present, on the past, in
anticipation of the future. For example, multiple customer and house non-buyer “time 0” views
make it possible to:

Create the analysis and validation files required for statistics-based predictive models.
Generate the data for all cohort analysis, including lifetime value.
Monitor changes in customer inventories, such as fluctuations in segment sizes over time.

Multiple “time 0” views also support data mining to understand how lifecycle changes affect
consumer purchase behavior. Direct marketers are lucky because, as a natural consequence of
running their businesses, they receive all of the detailed order, item and promotion history required to
perform lifecycle analysis. Retailers are not so lucky, unless they have a mechanism for identifying
customers and tracking their behavior. That is where Loyalty Programs come into play.

Let’s take a vertical industry – publishing – and work through a hypothetical example. Keep in mind
that, although the specifics are peculiar to publishing, the general concepts are universal across
vertical industries. We’ll begin with two assumptions:

A publisher of a magazine that is targeted to people in their 20's and 30's wants to understand
how changes in lifecycle affect renewal rates.

The publisher hypothesizes that renewal rates are adversely affected as subscribers begin to
raise families.

If the publisher’s hypothesis is true, then we would expect to see a drop in renewal rates as
subscribers move from multiple family dwelling units ("MFDUs”) to single family dwelling units
("SFDUs"), or from urban to suburban locations. With a properly constructed Marketing Database,
multiple subscriber cohorts can be analyzed over time for such relationships; that is, from when they
first signed up for the magazine though all of their subsequent renewal cycles.

People in their 20's and 30's are notoriously mobile. For example, from the time I entered the
workforce in 1980 to when I purchased my first (SFDU) home in 1988, I lived in five different

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apartments in three different cities and states. Without being able to recreate time-0 information, it
would be impossible to track this sort of customer movement.

The inability to track customer movement is the unfortunate outcome of any Marketing Database
designed such that, every time an address change is received, the previous address is over-written.
Such a database will never be able to support data mining to understand how lifecycle changes affect
customer purchase behavior, no matter how many years of history have been accumulated.

Does your Marketing Database over-write address information as notifications of customer
relocations are received? Are you even certain that you have a Marketing Database? Many
companies think they have a Marketing Database when, in fact, what they really have is an
Operational Database. I have seen this countless times when talking to prospective clients.

If you want to know if you have a true Marketing Database, then take the five-step data processing
test outlined in the June 18, 2010 Direct Listline article, “How Marketing Databases Differ from
Operational Databases”: http://directmag.com/lists/0622-lists-how/

marketing databases.

5


How Marketing Databases Differ from Operational Databases
By Jim Wheaton

Original version of an article that appeared in the June 29, 2010 issue of “Direct Newsline”

A Marketing Database must be able to perform all of the mission-critical analytical tasks required for
data-driven marketing. Many people think they have a Marketing Database when, in reality, what
they have is an Operational Database. An Operational Database supports essential “nuts and bolts”
tasks such as customer service, fulfillment and inventory management. But, it falls short in the
support of data-driven marketing analysis.

To determine if you have a Marketing Database, take the following data processing test. If you can
easily and rapidly execute the five tasks within the test, with no outside-the-system processing, then
you will know for sure that you have a Marketing Database:

FIRST: Examine the life-to-date detail for your customers as of June 1, 2009; that is, about a year
ago. This is known as a past-point-in-time (“time-0”) view, which will be impossible to recreate if
any of the following is true:

Some of your customers as of June 1, 2009 are no longer in the system.
Some of the historical data previous to June 1, 2009, for some or all of your customers, has
been deleted or overwritten.
You cannot exclude from your examination all historical data subsequent to June 1, 2009.

SECOND: Rank your customers from best to worst, as they would have been ranked on June 1,
2009. Do this by evaluating each customer’s year-ago view by whatever selection system you use;
that is, a statistics-based predictive model (or models), or some sort of rules-based logic such as
Recency/Frequency/Monetary (“RFM”) Cells.

THIRD: Divide the ranked customers into deciles; that is, into equal groups of ten.

FOURTH: For each decile, calculate the following subsequent performance; that is, from June 1,
2009 through May 31, 2010: Average Per-Customer Revenue and Average Per-Customer
Promotional Spend. Please note that the second will be impossible to calculate if you do not
maintain all-important promotion history for all your customers on a campaign-by-campaign basis,
regardless of whether a given customer did or did not respond to a given campaign.

If you can do all this, then you might have a Marketing Database. To know for sure, you
need to be able to do one last thing:

FIFTH: Simultaneously for each of three additional past-points-in-time – that is, June 1 for each of
the years 2008, 2007 and 2006 – create a standard File Inventory Report. The specifics will vary by
the type of business you are in, but invariably will include: 1) permutations of customer counts,
purchase rates and dollar amounts, and 2) year-over-year absolute as well as percent changes.

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Components of your File Inventory Report should also double as Key Performance Indicators
(“KPI’s”) that are closely tracked throughout the organization.

If you can do all this, then you really do have an environment worthy of being called a Marketing
Database. The reasons a Marketing Database needs to be able to do these five tasks are because:

Database marketing is, by definition, driven by deep-dive data mining.

Deep-dive data mining, in turn, requires the ability to rapidly recreate past-point-in-time
(“time 0”) views, and then manipulate and report on the data within these views. In fact, it is
common for multiple such views to have to be simultaneously recreated.

Without this ability, you will not be able to efficiently execute any cohort analysis such as
lifetime value. Nor will you be able to easily construct any statistics-based predictive
models.

Whether or not the Marketing Database and the Operational Database should be the same physical
resource is an entirely different issue. And, an entirely different article.

marketing databases.

7


The First Five Commandments of Database Content Management
By Jim Wheaton

Original version of an article that appeared in the February 1, 2007 issue of
“Multichannel Merchant”

This is the commencement of a quarterly column that will focus on best practices in data mining. We
define data mining as all of the analytical methods that are available to transform data into insight.
Examples include statistics-based predictive models, homogeneous groupings (“clusters”), cohort
analyses such as lifetime value, quantitative approaches to optimizing contact strategies across
multiple channels, and the creation of report packages and key-metrics dashboards.

What this Column Will Not Be About

We will not spend a lot of time comparing predictive modeling techniques and software packages.
Much has been written, for example, about the merits of regression versus neural networks. Having
participated in countless model builds, I speak first-hand to the fact that technique plays only a
secondary role in the success or failure of a predictive model.

Discussions about modeling techniques have always reminded me of the theological debate that took
place many centuries ago about how many angels can dance on the head of a pin. Today’s data
miners are fixated on their own pins and angels when they wrangle about techniques!

A by-product of this wrangling is the fantastic claims made by proponents of some of these
techniques. Unfortunately, such claims are pabulum for the gullible. The inconvenient truth, to
borrow a phrase from a prominent national politician, is that technique has very little impact on
results. There is only so much variance in the data, and the stark reality is that new techniques are
not going to drastically improve the power of predictive models.

What this Column Will Be About

The focus will be on the truly important issues; namely, just about everything else having to do with
data mining. For example, this month’s topic will be the significant improvements that are possible
for optimizing the raw inputs to the data mining process. The ultimate goal is to perform data mining
off a platform that we at Wheaton Group refer to as Best Practices Marketing Database Content.
This, in turn, supports deep insight into the behavior patterns that form the foundation for data-driven
decision-making.

General Characteristics of Best Practices Marketing Database Content

For starters, Best Practices Marketing Database Content provides a consolidated view of all
customers and inquirers across all channels. Examples of channels include direct mail, e-commerce,
brick-and-mortar retail, telesales and field sales. Sometimes – and particularly in Business-to-
Business and Business-to-Institution environments – prospects are included.

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Best Practices Marketing Database Content is as robust as the underlying methods of data collection
are capable of supporting. The complete history of transactional detail must be captured. Everything
within reason must be kept, even if its value is not immediately apparent. For example:

One multi-channel marketer failed to forward non-cash transactions from its brick-and-mortar
operation to the marketing database. This became a problem when a test was done to determine the
effectiveness of coupons sent to customers, which were good for free samples of selected
merchandise. The goal was to determine whether these coupons would economically stimulate store
traffic. But, because the corresponding transactions did not involve cash, there was no way to mine
the database for insights into which customers had taken advantage of the offer, and what the
corresponding effect was on long-term demand.

The Ten Commandments of Best Practices Marketing Database Content

There are Ten Commandments that, if followed, will ensure Best Practices Marketing Database
Content. Five are discussed this month, and the balance will be covered in the next column:

#1: The Data Must Be Maintained at the Atomic Level

All customer events such as the purchase of products and services must be maintained at the lowest
feasible level. This is important because, although you can always aggregate, you can never
disaggregate. Robust event detail provides the necessary input for seminal data mining exercises
such as product affinity analysis.

“Buckets” and other accumulations created from the data should be avoided. This is particularly
important for businesses that are rapidly expanding, where it can be impossible to audit and maintain
summary data approaches across ever-increasing numbers of divisions.

One firm learned the hard way about the need to maintain atomic-level detail when it discovered that
its aggregated merchandise data did not support deep-dive product affinity analysis. This is because,
by definition, it was impossible to understand purchase patterns within each aggregated merchandise
category. For example, with no detail beyond “Jewelry,” there was no way to identify patterns across
subcategories such as Watches, Fine/Fashion Merchandise, Bridal Diamonds, Fashion Diamonds,
Pearls/Stones, Accessories and Loose Goods.

#2: The Data Must Not Be Archived or Deleted

Within reason, data must not be archived. Likewise, it must not be deleted except under rare
circumstances. Ideally, even ancient data must be retained because you never know when you might
need it. Rolling off older data is perhaps the most common shortcoming of today’s marketing
databases; an ironic development because, unlike ten or twenty years ago, disk space is cheap.

Data mining can be severely hampered when the data does not extend significantly back in time.
One database marketing firm experienced this when it tried to build a model to predict which
customers would respond to a Holiday promotion. Unfortunately, all data content older than thirty-
six months was rolled off the database on a regular basis. Remarkably, it was not even archived. For

9


example, the database would only reflect three years of history for a customer who had been
purchasing for ten years.

The only way to build the Holiday model, of course, was to go back to the previous Holiday
promotion. This reduced to twenty-four months the historical data available to drive the model.
More problematic was the need to validate the model off another Holiday promotion; the most recent
of which had – by definition – taken place two years earlier. This, in turn, reduced to twelve months
the amount of available data. As you can imagine, the resulting model was far from optimal in its
effectiveness!

#3: The Data Must Be Time-Stamped

The use of time-stamped data to describe phenomena such as orders, items and promotions facilitates
an understanding of the sequence of progression for customers who have been cross-sold. This is
also true if customers are found to have purchased across multiple divisions during the incorporation
of acquired companies. Corresponding data mining applications include product affinity analysis and
next-most-likely-purchase modeling.

#4: The Semantics of the Data Must Be Consistent and Accurate

Descriptive information on products and services must be easily identifiable over time despite any
changes that might have taken place in naming conventions. Consider how untenable analysis would
be if the data semantics were so inconsistent that – say – “item number 1956” referenced a type of
necktie several years ago but umbrellas now. Also, the reconciliation of different product and
services coding schemes must be appropriate to the data-driven marketing needs of the overall
business, and not merely to the individual divisions.

#5: The Data Must Not Be Over-Written

Deep dive data mining is predicated upon the re-creation of past-point-in-time “views.” For
example, a model to predict who is most likely to respond to a Summer Clearance offer will be based
on the historical information available at the time of an earlier Summer Clearance promotion. The
re-creation of point-in-time views is problematic when data is overwritten.

A major financial institution learned this in conjunction with a comprehensive database that it built to
facilitate prospecting. After months of work, the prospect database was ready to launch. The
internal sponsors of the project, anxious to display immediate payback to senior management,
convened a two-day summit meeting to develop a comprehensive, data-driven strategy.

One hour into the meeting, the brainstorming came to an abrupt and premature end. The technical
folks, in their quest for processing efficiency, had not included in the database a running history of
several fields that were critical to the execution of any data mining work. Instead, the values
comprising these fields were over-written during each update cycle.

The incorporation of this running history necessitated a redesign of the prospect database. The
unfortunate result was a two-month delay, a loss of credibility in the eyes of senior management, and
a substantial decline in momentum.

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Final Thoughts

The next column will focus on Commandments Six through Ten of Best Practices Marketing
Database Content. In the meantime, consider whether your marketing database violates any of the
first five Commandments. The extent to which it does is the extent to which your firm’s revenues
and profits are being artificially limited.

marketing databases

11


The Second Five Commandments of Database Content Management
By Jim Wheaton

Original version of an article that appeared in the May 1, 2007 issue of
“Multichannel Merchant”

There are Ten Commandments of marketing database content management. This first five were
outlined in my February 1, 2007 column. This month, we will focus on the remaining five. But first,
a synopsis of the February column:

Data mining is enhanced, and often dramatically, when the source data is improved. The ultimate
goal is for data mining to be performed off a platform that we at Wheaton Group refer to as Best
Practices Marketing Database Content. This, in turn, supports deep insight into the behavior patterns
that form the foundation for data-driven decision-making.

Best Practices Marketing Database Content provides a consolidated view of all customers and
inquirers across all channels. The complete history of transactional detail must be captured.
Everything within reason must be kept, even if its value is not immediately apparent.

There are Ten Commandments that, if followed, will ensure Best Practices Marketing Database
Content. The first five as discussed in the February column are:

#1 – The data must be maintained at the atomic level.
#2 – The data must not be archived or deleted.
#3 – The data must be time-stamped.
#4 – The semantics of the data must be consistent and accurate.
#5 – The data must not be over-written.

The following are the balance of the Ten Commandments:

#6: Post-Demand Transaction Activity Must Be Kept

Post-demand transaction activity can include cancels, rebates, refunds, returns, exchanges,
allowances and write-offs. These are essential for important exercises such as the identification of
customers who will be less likely to make future purchases without remedial action. After all,
customers who are disappointed by unavailable, ill-fitting or damaged merchandise, or poorly-
conceived and improperly functioning services, will be less likely to purchase in the future. One
common data mining application is attrition modeling.

The capture of post-demand activity is particularly important in environments such as high fashion
women’s apparel where return rates can be as high as 40%. Often, customers with similar gross
purchase volume can have very different return rates. This, in turn, can make the difference between
a profitable customer and one who is a continuous money-loser. It makes sense for predictive
models to take such discrepancies into account when rank-ordering customers on expected behavior.

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Tracking post-demand transactions can be a challenge because it requires the transactions to be
retained by the underlying operational systems that feed the marketing database. Unfortunately,
many operational systems are not equipped for this task. Instead, post-demand transactions vanish
subsequent to a change in shipping status. For example, a “backorder” status will disappear once the
corresponding item has been shipped. The following hypothetical sequence of events illustrates why
this is problematic:

Assume that an operational system feeds a marketing database update process on the first and
fifteenth of every month. Also assume that a backorder is generated on June 2, and that the
corresponding shipment takes place on June 14. By definition, the customer had to wait twelve days
for the merchandise to shipped, which certainly is not ideal from a CRM perspective. If the
operational system does not retain backorder statuses, then the June 1 and June 15 “snapshots” that
feed the marketing database will fail to reflect the twelve-day wait. With only the June 12 shipment
reflected, an important aspect of the customer relationship will have been lost!

#7: Ship-To/Bill-To Linkages Must Be Maintained

Often, these correspond to gift-giver/receiver relationships. Ship-to/bill-to linkages allow targeted
promotions to extend the customer universe beyond those who made the original purchase. In fact,
savvy database marketers look upon giftees as qualified prospects. In this way, customer databases
can be used to drive targeted prospecting promotions, and often with formal data mining techniques.

#8: All Promotional History Must Be Kept

All promotional contacts across all available channels must be retained. This is necessary to rapidly
and accurately create the past-point-in-time “views” required for most data mining projects,
including predictive models. For multi-divisional firms, and especially those that have acquired
other companies, it is important to appropriately handle different coding practices.

One marquee, multi-billion dollar retailer with a substantial catalog/e-commerce division learned the
hard way the importance of including promotion history. Although it spends seven figures a year on
its CRM system, the underlying marketing database does not contain promotion history. As a result,
most data mining projects take a week longer than they should, because of the extraneous processing
required to overcome the lack of promotion history when creating analysis files.

#9: Proper Linkages Across Multiple Database Levels Must Be Maintained

For Business-to-Consumer (“B-to-C”) environments, individuals must be properly linked to
households. For Business-to-Business (“B-to-B”) and Business-to-Institution (“B-to-I”)
environments, individuals must be linked to sites, and sites to organizations. This allows the
calculation of accurate performance metrics such as promotional financials, and for understanding
the true nature of multi-buyers.

Such links also enable the tracking of pass-along response, and for innovative targeting programs.
For example, B-to-B and B-to-I direct marketers can monitor contract compliance across multiple
sites within large client organizations. In such instances, discounted pricing is predicated on
purchases not being made from the competition. With Best Practices Marketing Database Content,

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sites within client organizations can be identified that have not received any mission-critical
merchandise. Such sites may be out of contract compliance.

#10: Overlay Data Must Be Included, As Appropriate

For B-to-C, overlay data can be appended to create a complete view of customers, inquirers and,
when applicable, prospects. Likewise for B-to-B and B-to-I, “firmagraphics” can be added to create
a complete view of customers, inquirers, sites and organizations.

One form of B-to-C overlay data is demographics for existing individuals and households on the
marketing database, including date of birth, age, gender, marital status and presence of children.
Another is the identity of additional adults within households on the database, along with their
corresponding individual-level demographics.

For B-to-B and B-to-I, firmographics include SIC or NAICS Code, Number of Employees, and
Revenue. Also, additional individuals can be appended to sites that are resident on the database, and
additional sites to organizations.

One primary data mining application is the creation of profiles to “paint a picture” of customers and
inquirers. However, the possibilities go far beyond that, and are limited only by the imagination. For
example, date of birth can be employed to support birthday offers. Specifically, individuals with
upcoming birthdays can be targeted with offers of special savings to “treat themselves.” Also,
suitable gifts can be promoted to significant-others within the households. Such programs are
especially lucrative for retailers.

A Case Study of What Not to Do

Last year, Wheaton Group was approached about a potential data mining project by a well-known
gift-oriented, multi-billion dollar retail and direct marketing company that has been in decline. It
soon became apparent that the firm’s marketing database content would support neither the project
nor any other form of meaningful data mining. This is because:

Data is archived after 36 months and is difficult to resurrect. Some portions of the database are
maintained at the surname (“last name”) level and others at the individual level. For surname-level
database records, only one individual’s identity is retained. This means that if a husband orders the
first time, and then the wife orders – say – five subsequent times, the database will reflect six orders
from the husband. This is particularly problematic for a gift-oriented business. To complicate
matters, the database does not track bill-to/ship-to linkages and the corresponding gift relationships
that these imply, nor does it contain gender codes.

Often, the acquisition source is inaccurate, which renders problematic many worthwhile analyses
such as long-term value. Also, merchandise coding discipline does not exist, the Website does not
allow source codes to be entered, and customer records generally do not reflect post-demand
transactions such as merchandise returns.

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Promotion history is essentially unusable because the database tracks massive amounts of “spurious”
activity; for example, “event occurrences” such as records that have been sent to the service bureau
for National Change of Address (“NCOA”) processing. Also, there are significant problems with
tying promotion history to specific names and addresses, and email promotions are not tracked at all.

Finally, on the Retail side, distance-to-store calculations are based on imprecise ZIP-to-ZIP
Centroids. And, they reflect only the nearest store, not where the actual purchase activity has taken
place.

Clearly, unless the company rectifies the appalling state of its marketing database content, it will
have little chance of reversing its decline!

Final Thoughts

Consider whether you are working with Best Practices Marketing Database Content. The extent to
which you are not is the extent to which you are artificially limiting the size of your firm’s revenues
and profits. Also consider what methods you might employ to improve database content by
enhancing the functionality of your operational systems. There are all sorts of ways to do this. But,
that is the topic of a future article.

marketing databases

15

Marcus Tewksbury has 20 years of experience helping leading retailers and B2B’s harness the nexus of
technology, data, and marketing to drive growth and achieve financial results.

His focus is on Big, Fast Data and how its redefining the center of the marketing world. He partners with
the world’s largest retailers to help them design and build modern marketing infrastructures needed to
drive customer engagement in an omni-channel world.

Marcus applies his knowledge as a VP within Experian’s retail vertical where he leverages his deep
technology background to help clients develop new campaigns and programs built on the emergence of
addressable, cross-channel, audiences (digital TV, display, online radio, Facebook, etc.) whose
performance can be attributed and measured back at the individual or household level.

Prior to Experian, his experience spans both the client and agency side including six years starting and
running a retail manufacturing business and a focus on the marketing technology startup sector with an
IPO, two flameouts, and serving on two boards to his credit.

Marcus is a frequent speaker, having appeared at events for the American Marketing Association (AMA),
Canadian Marketing Association (CMA), Direct Marketing Association (DMA), Integrated Marketing
Summit, The Economist, Media Post, and Illinois Technology Association (ITA). His writing and
presentations have appeared or been cited in numerous publications like Mashable.com, USAToday,
Wall St. Journal, and the Word of Mouth Marketing Association (WOMMA). He has also been a guest
lecturer at numerous universities such as Georgetown, Northwestern, DePaul, U. of Chicago, and York.

More on Marcus’s thought leadership can be found on his blog http://themarketingmojo.com or on
SlideShare.

Database Systems Primer:
Deciphering Differences and
Determining Direction

Marcus Tewksbury
VP, Client Partner – Retail Vertical
Experian

1

Circa 2008 - 5 Years Ago (Database Platforms) 2

Circa 2008 - 4 Years Ago (Waterfalls, Multi-channel Segmentation) 3

Circa 2008 - 4 Years Ago (Scoring, Web2Lead, Email Nurturing) 4

Circa 2010 - 3 Years Ago (Package Proliferation) 5

Circa 2010 - 2 Years Ago (Shopping In a Foreign Land) 6

Circa 2012 – Today (Spheres of Ordered Chaos) 7

Complex Ecosystems – Example of Digital Display 8

Agenda

• Picking A Platform
• Putting It To Work
• Final Considerations
• Q&A

10

Picking A Platform

11

ESP

Campaign
Management

Marketing
Automation /
Lead Management

CRM

Cross Channel
Campaign
Management

12

What Is Cross Channel Campaign Management?

Customer Data CROSS CHANNEL CAMPAIGN PLATFORM

Content & Assets
30 days before
expiration
Recipient @ Customer
Inbound Dynamic/Variable Content
Database Web Site
Data Integration Business email mobile print
Secure FTP Rules
API Print Piece
&
Audience Selection
Logic
social
Activities web XML
media
Outbound & Responses Channel Criteria PURL
Data Integration
Secure FTP
API Event & Date no
Triggers submit? data
fulfillment call center
anywheredays
5
yes

Campaigns Mobile Email @

Sales & Orders Opens & Views
Bounces & Opt-Outs
Social Conversations Mobile Response (MO)
Following & Fan Of
Link Clicks Custom Responses

Recipient Actions

13

Someone is in the wrong room…

14

High
CRM

More than $500 Lead
Management

Customer LTV Cross Channel
Management

Campaign
Management
ESP
Low

Low # Contacts High

More than 50,000

Size of Database (Customers on File) Vs. Customer LTV 15

Price or
Operational Focus
> 100,000
Omni-Channel
Customers Campaign
Relationship Management
Focus
< 100,000
Customers

> 250,000
Channels Served?
Customers
Price or
Operational Focus
< 250,000 Cross Channel
Online Only
Customers Management
Relationship
Focus
Offline Only
High

CRM
Lead
Managem
ent
Customer LTV

Cross
Channel
Manageme
ESP
nt

Campaign
Managem
ent
Low

ESP

Low # Contacts High

High Volume Plays / Direct To Consumer 16

250,000
Contacts

10,000
Contacts Lead
Website Management
< 10,0000
Contacts

Primary Channel
10,000 Cross Channel
Contacts Management
Other
< 10,0000
Contacts
High

CRM
Lead
Managem
ent
Customer LTV

Cross
Channel
Manageme
CRM
nt

Campaign
Managem
ent
Low

ESP

Low # Contacts High

High Value Plays / Considered Purchase or B2B 17

Putting It To Work

18

Top Level Goals

• Attract new customers
• Encourage customers to buy more
• Encourage customers to buy more often
• Encourage customers to be loyal
• Win back customers who’ve defected

19

Typical Consumer

20

CONSUMER
REPOSITORY

Selecting & Prioritizing Channels Appropriate for Customer Base 21

Michael… Personalized
Experience

23

Website Personalization will be driven by
algorithmic black box point solutions or
behaviorally rich, rules driven platforms –
namely ESP, campaign management, or lead
management ones.

Source of Intelligence 24

Community created content is most trusted
and sought after by fellow customers. Stored
and curated on specialty tools, this information
can be shared with platform apps for triggered
communication and message amplification.

Leveraging Community Content 26

Enabling agents to
connect with
customers in the most
convenient format
armed with a complete
history of the customer
relationship makes for
a richer customer
experience.


Part of the SocialCRM strategy can
leverage integrated technology
capabilities to help a consumer
connect and share their brand
experience with like minded
individuals.


The Future Of Targetable Marketing… Isn’t As Futuristic As You May Think
31

Final Considerations

34

Top Level Goals

• Attract new customers
• Encourage customers to buy more
• Encourage customers to buy more often
• Encourage customers to be loyal
• Win back customers who’ve defected

35

Awareness Consider Acquisition Retention
Mode Medium Awareness Consider Acquisition Retention
Std. Dev. Std. Dev. Std. Dev. Std. Dev.

Mobile 1.11 -1.00 2.44 0.28 2.11 0.23 1.56 -0.07
Apps 1.00 -1.11 2.50 0.34 2.00 0.12 2.00 0.37
Mweb 1.00 -1.11 2.00 -0.16 4.00 2.12 3.00 1.37
QR 1.00 -1.11 3.33 1.17 2.00 0.12 1.33 -0.30
SMS 1.33 -0.78 1.67 -0.50 1.67 -0.22 1.00 -0.63
Offline 2.84 0.73 1.89 -0.27 1.74 -0.15 1.74 0.11
Direct 2.50 0.39 2.25 0.09 3.00 1.12 2.25 0.62
Experience 1.20 -0.91 2.00 -0.16 2.00 0.12 2.00 0.37
Image 3.00 0.89 1.75 -0.41 1.00 -0.88 1.50 -0.13
Mass 4.33 2.22 1.67 -0.50 1.17 -0.72 1.33 -0.30
Online 2.38 0.26 2.63 0.46 2.06 0.18 1.44 -0.19
Display 2.75 0.64 3.50 1.34 2.75 0.87 1.50 -0.13
Email 1.25 -0.86 2.25 0.09 1.75 -0.13 2.00 0.37
Search 3.00 0.89 2.40 0.24 1.60 -0.28 1.00 -0.63
Website 2.33 0.22 2.33 0.17 2.33 0.45 1.33 -0.30
Social 2.15 0.04 1.85 -0.32 1.23 -0.65 1.46 -0.17
Sponsorship 3.00 0.89 1.00 -1.16 2.00 0.12 2.00 0.37
Syndication 2.33 0.22 1.33 -0.83 1.00 -0.88 1.67 0.04
Targeted 1.50 -0.61 2.25 0.09 1.25 -0.63 1.00 -0.63
Word Of
Mouth 2.25 0.14 2.25 0.09 1.00 -0.88 1.50 -0.13
Total 2.11 2.16 1.88 1.63

Channel / Customer Life Cycle Stage Matrix 36

Mode Medium Implementation Mode Medium Implementation Mode Medium Implementation
Word Of
Offline Direct Mail
Online Website 1st Party Domain Social Mouth Forums
Offline Direct List Rental Word Of
Online Website Affiliate
Offline Direct Phone Social Mouth FB Timeline
Online Website Digital Radio Word Of
Offline Direct Catalog Social Mouth Tweets
Online Email Lead acquisition Word Of
Offline Mass TV
Online Email Nurturing Social Mouth FB Fan Pages
Offline Mass Radio Social Syndication Content
Offline Mass Print Online Email Operational Social Syndication Games
Social Syndication Widgets
Offline Mass FSI Online Display Media Plan
Offline Mass News Paper Online Search SEM - PPC Social Sponsorship Blogger

Offline Mass Digital TV
Online Search SEO - Organic Social Sponsorship Celebrity
Offline Experience Event Social Targeted Mentions
Online Search Local
Social Targeted Direct Messages
Offline Experience Instore
Online Search Vertical Social Targeted FB Stories
Offline Experience POS Social Targeted Sponsored Tweets
Online Search Link Building Mobile Mweb Microsite
Offline Experience Loyalty
Mobile SMS Text-to-Join
Offline Experience Sky writing Online Display Media Plan
Mobile SMS Text-to-vote
Offline Image PR Behavioral Mobile SMS SMS / MMS
Online Display Retargeting Mobile Apps Catalogs
Offline Image Placement
Mobile Apps Games
Offline Image Sponsorship Online Display Segment Targeting Mobile QR Price comparison
Mobile QR User Reviews
Offline Image Sweepstakes Online Display Direct Targeting
Mobile QR Social Share

Choosing the Relevant Capabilities 37

Some Assembly Required?
38

Deployment model
impacts pricing and
organizational
support
requirements

39

Thank you

Marcus Tewksbury
+1 312.404.4835
Marcus.tewksbury@experian.com
@tewksbum

TOC

1

2

3

4
THE EXPERIAN MARKETING
5
INNOVATION REPORT 2012
6 What every marketer needs to know for effective cross-channel marketing innovation

7

8

9
Cross-Channel
10 Optimization
Multi-Channel
Channel Marketing
11
Optimization
12 Channel
Execution
13

BIO

TOC

1 table of
2 contents
3 SECTIONS
1. Introducing the Experian Marketing
4 Innovation Report 2012

2. The Marketing Technology Ecosystem
5 3. Customer Database

4. Data Capture
6
5. Data Integration

7 6. Analytics and Insights

7. Programs, Performance,
8 and Measurement

8. Campaign Management
9 9. Personalization / Recommendation
and Relevance
10 10. Traditional (Direct, Experience, Image, Media)
11. Digital (Display, Search, Web, Email)
11
12. Mobile (Direct Marketing, Mobile Web, Apps, Scanning)
12 13. Social (WOM, Community, Monitoring, Syndication)
CLICK ON SECTION TO JUMP TO DETAILS
13

BIO

TOC

1 Introducing ission
the Experian Th e core m
eting
of mark o Connect
Marketing
2 t
remains: tomers.
s
with Cu t
3
Innovation os
But alm else has
everythin
g
4 Report 2012 changed.

5
Innovation and Marketing Technology Following Customers Across Channels
6 Each year brings fresh challenges for marketers. Technology Media consumption is fragmented, so marketers must chase
emerges, the media landscape evolves and consumers consumers across multiple channels utilizing campaigns that
expect more from the products they buy and the media they are increasingly difficult to integrate. Investing in platforms
7 consume. So marketers must innovate continuously; we must to aggregate audiences helps but there is no simple or
actively explore, experiment, evaluate and optimize. comprehensive way to reach across traditional, digital, mobile
and social channels.
8 Technology drives much of the change in media channels
and consumer behaviors. Yet, technology also presents In addition to being harder to find, today’s consumers are
marketers new opportunities and a vast array of choices. The more demanding of their brands. They expect brands to listen,
9 core mission of marketing remains the same: to connect with interact, behave well and most critically, be relevant. “Don’t
customers. But almost everything else has changed. waste my time” is the mantra because we are busier than ever,
have more choices than ever, and get bombarded with more
10 We will review the challenges, opportunities and methods for messages than we can possibly make sense of.
achieving better results in the ever-changing world of digital
marketing. A typical path in the customer’s journey helps illustrate the
11 This report helps marketers frame the decisions they need to
challenges of cross-channel, or multiple touch-point marketing.
Effective marketing strategies must connect many dots in order
make, and offers strategic guidance in selecting and applying to lift sales. Each stage of the customer’s path to purchase
marketing technology.
12 requires a different tactic to plan, manage and execute.

13

BIO

TOC

DISPLAY
1 ADS

EMAIL DIGITAL
2 DISCOVERY

CATEGORY
3 SEARCH

WEB
WEBSITES
4 RESEARCH

SOCIAL MEDIA
5
SHARE
PRODUCT
6 SEARCH
WEB RETAIL
SALES SALES
STORE
7 COMMERCE
PRODUCT
EXPERIENCE
CALLCENTER/
8 WEB SUPPORT

9
Imagine a person who is interested in guitars and related What if she then checks online, finds peer reviews and figures
musical equipment, but is unaware that a particular brand out how it works, and becomes not only a satisfied user but a
10 offers a new kind of tuning device. How would this prospective delighted customer? How should the brand turn this enthusiasm
buyer and brand connect – how would they find each other? into advocacy – and turn this single transaction into five more?
The buyer may search online for other products in the A variety of new techniques and technologies is required to
11 category, or browse content on related topics. Moreover, it’s address this cross-channel, digital path to purchase.
likely the buyer would be discussing music with friends on a
12 social network, or sharing a video.

13

BIO

TOC
Innovating along the Marketing The need for cross-channel optimization is evident in the
Sophistication Curve declining performance of traditional media and marketing
1 methods. According to Gartner, “Mass marketing is no longer a
Marketers need to innovate and leverage new technologies, long-term strategy. Mass-marketing campaigns have a 2 percent
but to what end? How do you distinguish an advancement response rate and are on the decline, whereas by 2015, digital
2 from experimentation with “a shiny new object?” Experian strategies, such as social and mobile marketing, will influence at
views innovation in the context of the Marketing Sophistication least 80 percent of consumers’ discretionary spending.” (Gartner,
Curve™. The curve provides an intuitive guide for brands to March 2011)
3 evaluate their level of cross-channel marketing sophistication
and suggests opportunities for improvement. Moreover, marketers are being held accountable for ROI on
marketing spend. They need to re-calibrate their systems
4 We make a sharp distinction between multi-channel versus of measurement to know what’s working and what’s not in
cross-channel marketing. “Multi-channel” means being present this complex, multi-channel mix. The attribution of response
and active in multiple channels. “Cross-channel” means being by channel and across channels is a huge challenge, yet
5 consistent and coordinated across channels. measurement is essential to both motivating and guiding
innovation.
It turns out “cross-channel” is a whole lot harder! Measuring
6 progress in cross-channel sophistication provides strategic
direction about where to invest in technology and processes.

7 Cross-channel optimization requires an integrated
approach across:
-- The customer journey
8 -- Company silos Multi Channel
Cross Channel
Optimization
Marketing
-- Disparate systems Channel
9 Optimization recognizing
channel
triggered and drip
campaigns
preference

Channel scoring, modeling, response revenue / outcome
10 Execution and advanced
segmentation
attribution causation

11 single channel,
fire and forget
faster cycle
times
enabled across
multiple platforms
personalized
interaction

12 file based list Persisting results voice of customer consolidated view
processing over time per channel of customer

13

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TOC
Customer-Centric Marketing Deeper Customer Insights
1 As marketing complexity continues to increase, the simplest What makes it so difficult to gain customer insights? After all,
way to manage progress is to focus on the customer. What we leave our digital footprints all over the web…and there are
will improve the customer experience and journey? How can vast stores of both online and offline consumer data available.
2 marketers better serve the needs of customers? A customer-
centric approach makes it imperative to strengthen two Ironically the data is both our greatest strength, and also our
disciplines above all else: greatest weakness. The key problems with data fall into five
3 areas:
-- Customer Insights – understand both the “persona” and
the person -- Fragmentation – the classic silo problem. Speaking through
independent channels (a.k.a. email, print, display, mail, etc.),
4 -- Relationships – consistent, coordinated, sustained transacting through others (e.g., POS, website, call center,
interactions and engagement etc.) and being unable to pull it all together.

5 -- Hygiene – data is often partial, duplicated, incorrect or
outdated. Maintaining actionability requires an ongoing data
quality process.
6 -- Scale – the impact of digital ubiquity is just beginning to be
ers
consum
felt. A large implication of that is an exponential explosion in
Today’s ands to
the amount of available data. What had been difficult problems
7 r
expect b ract,
to solve for in the past are crippling to many current marketing

te
technologies. Standard data sets of the future will dwarf the
listen, in ll, and — largest of today.
e
behave w ically— be
8 -- PII – customer’s sensitive, private or confidential data must be
it
most cr
safe guarded against fraudulent use. It also must be used in

relevant.
appropriate and permissible ways.
9
-- Speed – hindsight is always 20/20. Tackling the above four
issues in a time frame relevant to decision making is another
10 large challenge. Today, insight must often be applicable to
addressing questions in web page load type times.

11 Consumers want brands to “listen to me and act like you know These well-known hurdles to building customer insights are
me.” Don’t send me a catalog for children’s products when addressed by recent developments and offerings in the marketing
you know I don’t have kids. Do recognize I’m one of your best technology space, and will be discussed later in the report.
12
customers when I visit your site looking for accessories.

13

BIO

TOC
Stronger Customer Relationships
1 Every brand aspires to create a meaningful relationship with Two way interactions are
prospects and customers. In the past, much of the relationship critical for brands to deliver
was shaped by the brand’s one-way communication, but now rich, relevant experiences.
2 more than ever it comes from two-way experiences.

In addition, customers experience the brand through in-store
3 interactions, dialog, conversation, services, use and peer-to-
peer social interactions. The two-way interactions are critical
opportunities for brands to actively engage in the conversation
4 and deliver rich, relevant experiences. If the touch points are
inconsistent, customers feel confused or dissatisfied. The
difficulty for marketers is speaking in the same voice across
5 very different channels.

Customers want to know whether to trust a brand, which
6 depends on whether the brand behaves like a friend or like
a stranger. Do I have to re-enter all my personal information
each time we meet? Do you remember my preferences from
7 the last time I said I’m not interested in leather sofas?

8

9
To behave consistently brands must not only house a lot of
customer data safely, but they need to curate and organize
10 that information in a way that campaigns, offers, programs,
and content are personalized, helpful and intelligent. It is both
a technological and an organizational challenge. A unified
11 customer experience depends considerably on a unified
corporate commitment to the customer experience.

12

13

BIO

TOC
Customer Engagement Framework
1. Listen to the customer across channels to construct
1 Relationships are built over time through a series of customer a unified view
engagements and experiences. If the experiences are mostly
positive, the relationship grows stronger, and vice versa. 2. Analyze in a variety of ways to understand
2 customer needs
To engage effectively with customers across channels and over
time, brands must carefully plan how to advance a customer 3. Plan who, how and when to engage with customers
3 through each stage of their lifecycle. We believe there is a
consistent, repeatable approach all marketers can employ 4. Speak engage consistently across channels in a
to optimize the customer experience. We view this four step relevant voice
4 methodology through the following lens:

5
STORE MODELING CAMPAIGN DESIGN

6 DIRECT
EMAIL
To be clear, the
TV customer engagement
7 framework is not a
SYNDI- linear series of steps.
CATED It is a process that
8 CALL needs to be repeated
CENTER multiple times across
INTEGRATED
PROFILES CONSUMER
OPTIMIZED channels and the
EMAIL
customer lifecycle.
9 INSIGHTS
OFFERS

WEB-
SITES BEHAVIORAL ANALYSIS TARGETING

10 SEARCH

SOCIAL
MEDIA
11

12

13

BIO

TOC
Acquiring and Retaining Customers Here we’ve used an example that talks about growing or
reactivating a loyal customer. For acquisition efforts, the desired
1 The Customer Engagement Framework helps companies more outcome may not be a purchase, but first building desire. With
effectively acquire top prospects and retain best customers. the emergence of “content marketing” you will see this more and
more where specific campaigns are aimed at moving a customer
2 As a framework, how exactly these outcomes are realized
to the next stage in the lifecycle. A common two step approach:
depends upon the specific needs of the situation. Continuing
Build awareness and educate, then differentiate and sell.
the guitar example from earlier, say the brand now wants to
3 promote a new line of velvet straps to past, repeat purchasers.
A cross-channel optimized marketer would begin by listening
(collecting and combining all possible data sources germane
4 to the segment), then analyzing (turns out the heavy metal
sheet music purchasers also have a penchant for faux leopard
skin print pants and guitar covers they purchase online), come
5 up with a plan (let’s highlight the leopard print straps and use
email and addressable display to make an online only offer),
and finally speak (deliver a coordinated campaign across email
6 and display that handles sequence and cadence of delivery at
the individual level while tuning the specific messaging to the
particulars of the audience – the Metallica versus Distrubed
7 fans).

8

work,
9 As a frame these
actly
how ex
es are
10 outcom epends
d
realized specific
e
11 upon th the
f
needs o
n.
12 situatio

13

BIO

TOC
Top Ten Tips for Cross-Channel
Optimized Marketing
1
1. Build a comprehensive, unified view of
2 prospects and customers
2. Link behaviors and attributes across all
3 channels and data sources
3. Identify and address specific customer
4 segments
4. Personalize messages and interactions
5 5. Engage in a consistent voice across
traditional, digital, mobile and social
channels
6
6. Respect preferences through registrations
and opt-ins
7
7. Secure personal, sensitive and confidential
data
8 8. Center the marketing process around
customers - not around channels or
campaigns
9
9. Strengthen relationships by optimizing the
experience to customer life stage
10
10. Correctly attribute sales to marketing
investments
11

12

13

BIO

TOC

The Marketing
Data: Everything that is possible in the world of marketing is
dependent upon the data that flows through the system. From
1
Technology
response to transactional, or 3rd party syndicated and everything
in between, data is the life blood.

2
Ecosystem Integration: What is possible with the data, however, is
constrained by how it can be combined to expose its most
valuable aspects and where it can be brought to bear. The
3 Making everything discussed so far a reality is entirely integration is like vascular system or plumbing bringing the data
dependent on having the right technology infrastructure in where it needs to be.
place. While you may have the know-how to be cross-channel
4 Intelligence: The value of data is quantified by the number and
optimized, if your tools can’t deliver you may be stuck with single magnitude of actionable insights it can produce. The brains of the
channel optimization or even basic channel enablement. operations, this is multiphased piece of how great insight is built
5 As most marketers are not technologists, this presents a and the sequence and cadence of engagement laid out.
significant challenge. Knowing what pieces are needed and how Interaction: As many week-end golfers can attest, knowing what
they fit into an overall solution is key. At the highest level, the
6 marketing technology world can be broken into three key hubs
to do and doing so are often two very different things. Being able
to act on insight and delivery of a relevant, timely experience is a
built upon a foundation of data. large challenge.
7

8
INTERACTION

9
INTELLIGENCE

10 INTEGRATION

11 data

customer
intelligence
12 platform

13

BIO

TOC

customer
Implementing a customer database usually requires
cooperation across IT and marketing, as well as a deep,
1

database
shared perspective between the business and technology
discipline. Whether internally contracted, or via external
partner, this would be an ideal place to deploy a Chief
2 Marketing Technology Officer (CMTO) type of resource.
A customer database is critical to nurturing relationships and What is most important here, of course, is what goes into
3 driving relevant, coordinated multi-channel campaigns. If the database – i.e., the data itself. This covers not only
you don’t have an integrated, multidimensional view of the the behavioral response and transactions you can capture
customer that is accessible from multiple downstream systems first hand, but also additional data that can be acquired or
4 you are going to struggle with expanding relationships. appended by 3rd parties. There is literally an ocean of data
out there that you can tap into. Top websites, new home
purchases, credit profile, recent purchases and all sorts of
5 Enable a customer-centric approach to marketing with
other attributes valuable to driving models and segmentation.
a customer database.
An effective data strategy will encompass pieces from all
6 -- As marketing increasingly becomes one-on-one, a customer available sources.
database becomes a corporate, strategic asset for
marketing and sales.

7 -- Effective segmentation and personalization requires
intensive analytics and reporting. Customer Database ≠ Marketing Database
8 -- Customer insights must be analyzed continuously for an up- In common marketing parlance, a marketing database is
to-date view of customer preferences, needs and feedback. associated with a purpose built database to support either
mail circulation or email. This is not what we are referencing
9 as a customer database. These point systems were built to
“Customer database” refers both to a concept and a
solve specific problems and do not meet the scale, speed or
technical implementation, which typically consists of multiple
integration requirements of a customer database. This is not
10 repositories. You may actually have 2 or 3 physical databases
to say they do not have a role to play, this traditional view of
serving slightly different purposes running behind the scenes
“database” plays a large part in “campaign management” and
to provide what the business actually needs. Two typical
select channel execution.
11 scenarios are either to build around a central CRM system
(e.g., Salesforce.com, MSCRM, etc.) or an operational
marketing database tied to a dominant channel (e.g., email
12 platform tool or campaign management tool).

13

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TOC
Acronyms / Terms:
1 System of Record (SOR) – An important concept for any
customer database environment. At the core, one system
must be anointed as the central one that all others must be
2 related to.

Marketing Service Provider (MSP) – specialized agencies
3 that grew up and matured around the circulation and
direct marketing businesses. Skilled at building customer
databases, slinging around very large data sets and
4 outputting lists for fulfillment via direct and email channels.

Chief Marketing Technology Officer (CMTO) – far
5 more than any other across the multitude of business
disciplines, marketing’s role and processes are being
redefined by technology. Understanding how these tools
6 and emerging channels can be best leveraged requires a
blend of capabilities not often found in the IT or marketing
organizations. Thus is born a new role in which these two Vendors: CRM Platforms
7 divergent skill sets are brought together.

Data Append – you can buy 1000’s of additional data points
8 about your known prospects and customers. When you
purchase this data it is appended to the existing records you
have on file. Vendors: MSP Platforms
9
CRM – other than being one of the most overused and
hackneyed terms in marketing, it’s also still one of the most
10 important. When you look at all the flavors over the year from
eCRM to SocialCRM, what is important to note is that it’s
always about taking all that is knowable about a customer
11 and organizing the data around them. The customer is the Vendors: DATA VENDORS
nucleus – not the channel or campaign.

12

13

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TOC

Data
Acronyms / Terms:
1 Cookie – is a uniquely identifiable tracking device that is dropped

2 Capture by a website domain (e.g., amazon.com, ebay.com, etc.) to a
browser on a given machine. These cookies persist over time
and are a central component in maintaining state across session
(a.k.a., make rich, interactive web experiences possible). They
Data cannot be integrated or analyzed if it is not properly also are the lynch pin to tracking users online.
3 captured. For many marketing platforms this can be
problematic. Since the rise of mass media, the primary focus of Key / Cell Codes – the world of direct marketing ultimately
marketing activities has been on just delivering the impression. means breaking down your database into multiple smaller lists
4 This is quite rational and explainable because in an analog for mailings or other marketing communications. Each of these
world, or an unsophisticated digital one, capturing response smaller lists, which could receive different offers, versions, etc.,
data is difficult if not impossible. is assigned a specific code that identifies it in each successive
5 “drop” or “flight.”
Going across the spectrums of traditional, digital, mobile and
social channels, there are a multitude of response capture
6 mechanisms. Ranging from things like coupon key codes
the
accept
We now
to 3rd party cookie tracking, there can potentially also be
is
learning
ct that
multiple approaches within a single channel. Breaking down
fa ss
7 g proce
a lifelon abreast of
the complexity of how these can be woven together into an
ing
of keep
integrated, consistent approach to capturing data is beyond the t
the mos
scope of this report. The intent here is to highlight the need to
cha nge. And is to
8 task
pressing
make sure it is addressed in your strategy.
to
ple how r
te ach peo cke
This is one area where our direct marketing brethren may eter Dru
9 have a slight leg up. The notion of response capture has long learn. -P
been a staple of direct marketing. In the digital space, the
capability has been there, but there hasn’t been as pressing a
10 need to address it because the financial cost of each additional Vendors: WEB ANALYTICS
impression is so low.

11
*all marketing automation tools incorporate web tracking as well

12 There are too many to cover in detail. Each channel has its own cadre of options.
Suffice to say in the digital and social spaces quite a bit can be traced to cookie
tracking which has its roots in web analytics.

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Data
that spans the entirety of possible audience interactions. It’s
inefficient and slightly unrealistic for an independent company,
1

Integration
regardless of size, to try and tackle this. You need to find a
partner who specializes in building these types of repositories.

2
Acronyms / Terms:
One of the most common and largest stumbling blocks
3 preventing many marketers from realizing the potential of their Master Data Management (MDM) – a combined approach for
customer relationships is the inability to integrate their siloed collecting data from across your organization and combining it all
data back into a consolidated view of the customer. This is not in a single repository. This enables marketing to pull campaign
4 by happenstance; breaking down these walls is complicated data out of its channel silo and view the totality of impact on the
work and requires leveraging 3rd party datasets to realize customer.
truly multi-channel integration. Another issue surrounding data
5 integration is that it is by far the most technically complex piece Customer Data Integration (CDI) – a specialized subset of
of the multi-channel marketing because it is strictly a technology master data management. CDI involves merging various lists
challenge. to find commonalities which can identify who that customer is
6 across each system and customer list.
Finding a technologist who spans all the required technology
stacks presents another challenge. It is a pretty rare person who Linkage – the process of linking the disparate files of customer
7 can pull all these pieces together. A Chief Marketing Technology data by key identifiable data points such as name, address,
Officer (CMTO) is important to effective data integration. email, phone, handle, etc.
A CMTO is a person who understands marketing data, the
8 systems from which to source that data and the applications
that will ultimately use that data. If you don’t have that person in
Vendors
your organization, we suggest finding a strong data integration
9 partner who understands marketing data.

Linkage is a critical part of data integration for marketers. While
10 many marketers can find this concept difficult to grasp, noted
CMO of West Marine, Lynn Ferguson, is famous for saying,
“keep it second grade simple.”
11
So, while technically speaking, linkage can get quite complex,
there is no magic to it and ultimately building linkages across
12 traditional, digital, mobile and social channels becomes a brute
force operation. What is required is a master data universe

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ANALYTICS
Types of Insight
1 We can know things like this at the individual (personally

and identifiable) or segment level:
-- Channel engagement and preference

insights
2
-- Online activity
-- Attitudes, opinions and beliefs
3 -- Psychographic and lifestyle
Though a master customer database delivers a single view of -- Demographics
the customer, the database itself does not solve any marketing -- Social media insights
4 problems like better audience segmentation or more targeted
messaging. These optimization problems require insights
5 derived through analytics. Marketers often get started by simply Acronyms / Terms:
gaining greater visibility and access to aggregate customer data
Dashboard – most folks are pretty familiar with the gauges,
through static reporting, dynamic dashboards, and tools that
thermometers, etc., that grace our sales and executive
6 allow “what if” analysis on the fly.
reports. Unfortunately, however, not as many have experience
As a marketer’s sophistication rises, they implement increasingly with really well-conceived measurement metaphors. For a
advanced analytic tools based on statistical models like: dashboard to be effective, it should appropriately summarize
7 operational data in metrics or ways that make it easy to spot
Multi-Channel Response Models - Leverage past campaign outliers or leading problem indicators. Dashboard reports
responders in model development to predict likelihood to shouldn’t just be a series of pretty charts and graphs, but
8 respond by channel. Enables appropriate allocation of mail. should focus on how the results drive changes to strategies
Email circulation to cut costs and boosts campaign response and campaigns.
and revenue 20-40%.
9
Product Affinity Models - Next Best Product Models predict the Vendors: SOFTWARE
best product to offer. Replenishment Models rank the likelihood
10 to replenish consumable products.

Delta Models - Delta Models predict incremental effect of a
11 marketing promotion and identify target audiences who are likely
to respond only when being promoted. Vendors: SERVICE
12 All three types of tools can be integrated to drive increased response,
reduce promotional markdowns, and drive customer engagement.

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Programs, towards that goal. Next, you must attribute response by
channel, which is a huge challenge, but essential to both
1
Performance, motivate and guide marketing innovation.

2 And Measurement What differentiates performance and measurement from
analytics and insights really boils down to the targeted output.
The techniques, approaches and inputs between the two are
While ROI can be calculated at many points, where it has the the same. As shown in the table, this may be the one place
3 most upside or strategic application is at the program level. in the whole conversation where it’s appropriate to highlight
Not all marketers apply the word “program” the same way. non-customer-centric metrics. Best practice would dictate
Here, what is meant is the overarching customer strategy that there should certainly be some customer orientated ones
4 the will span multiple campaigns, channels, creative, offers, (e.g., LTV, voice of customer, etc.) in the list, but at some point
versions, drops, and flights over a set period of time. It’s about the metrics also have to help evaluate the effectiveness of a
setting a vision for where you want to go with your customer specific campaign.
5 relationships and measuring each of your activities’ contribution

6
Channel EXPOSURE METRICS ENGAGEMENT METRICS OUTCOME METRICS
7
Conversions, Cost per
Display Advertising CPM Clicks, Cost Per Click Conversion
8
Conversions, Cost per
Paid Search CPM Clicks, Cost Per Click Conversion
9
Paid Media Equivalent Interactions, Viral Spread,
Social Activation
10 Cost, Reach Buzz

Clicks, Interactions, Time Conversions, Cost per
11 Site-Side Content Spent, Cost per Interaction Conversion

Open Rate, Click
12 eMail Response Rate, Cost per Conversions, Cost per
Conversion
Open, Cost per Response

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You always should be asking things like: Media Mix Modeling (MMM) – a very important aspect of
program planning is budget allocation across channels. As with
1 -- Which channel was most effective?
financial portfolio optimization or operational CPM, the intent is
-- Which creative version worked best? to model the process and solve for a case that maximizes the
dependent variable, in this case, impressions.
2 -- Which campaign drove the highest response rates?
Multi-Channel Response Attribution – speaks to the ways
-- Where did I have the best cost per acquisition? marketing activities can be related back to sales performance.
3 For example, in direct mail campaigns, coupon codes can be
tracked at the POS as they are scanned. Later, those codes and
corresponding transactions can be combined with campaign send
4 Multivariate Testing (also A/B Testing) data to analyze response and performance.

From a testing perspective, many of the happenings in the digital
5 world are retreads of things long established in the traditional
direct world. One example of this is the notion of displaying Vendors: MRM
various versions of creative or copy during the same campaign
6 to assess which version works the best. Marketers have been
doing this for ages with controls and hold-outs, but in digital it
can be done in real-time with multitudes of versions for less cost.
7 Vendors: Programming Vendors: measurement

8
Acronyms / Terms:
9 Response Allocation – studying data to ascertain what
marketing elements caused the customer to make a purchase.

Marketing Resource Management (MRM) – functions as a
10
calendaring and resource planning tool for marketing programs.
The focus is planning versus execution in much the same way
11 as a project management tool works.

12

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1 CAMPAIGN Acronyms / Terms:

MANAGEMENT
Lead Nurturing / Drip Marketing – from an audience
standpoint this refers to the sequence of related messages that
is delivered over a period of time. From the tool side, it refers
2 to the ability to schedule said series and queue the messages
Where program management is about creating the overall to be delivered at the appointed time.
battle plans, campaign management is about loading, aiming
3 and firing the guns. What seems to be a point of confusion
Scoring – One of most important segmentation schemes
addresses the ability to rank customers by any number of
for many marketers, however, is that the term “campaign” possible combinations of attributes and behaviors. Like a
4 can mean different things depending on a given marketers’ mathematical function, the rules are applied to each customer
focus. For example, a traditional circulation marketer would to calculate a score that can be used to rank the customers.
define it as the segmentation and coding of a distribution list,
5 while an online display marketer would consider it to be the Life Cycle Marketing – essentially these are drip campaigns,
varying flights of creative that are deployed to the preselected renamed to describe specific types of common campaigns.
publisher sites. As the walls between channels continue to A very common example would be a “Welcome” campaign
6 erode and eventually crumble, marketers will certainly come to whereby a new customer (or loyalty member) receives a set
find a commonality in the term. number of targeted introductory communications via email
and mail.
7 A common way to view campaigns is a visual workflow that
depicts the various stages / treatments / or flights of messages
in their relative order. More sophisticated tools enable for
8 branching decisions whereby different audiences receive Vendors: TRADITIONAL
a different message based upon scoring, firmographic,
behavioral response or other segmentation scheme. Another
9 aspect some of the tools support is the ability to respond to
customers. As opposed to marketing to a pre-established
list, in triggered-campaigns customers self select into the
10 campaign by an action, like completing a web registration, or Vendors: Marketing Automation
redeeming a coupon at a POS at which point they receive a
message, or series of messages, in response.
11

12

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PERSONALIZATION/ Acronyms / Terms:
1
RECOMMENDATION
Blackbox Analytics – like IBM’s Watson from Jeopardy fame,
knowing what to show when is based upon a complex algorithm of

2 and relevance processing and relating massive volumes of data. Many of these
approaches will be self tuning based upon data input and be
obscured from the marketer.
Of all the topics covered in this report, this is the one that is the
3 least mature. As marketers get more plugged into the concepts
Personalization – is driven by static, known attributes. Filling in
the right name in a salutation (i.e., Dear Mr. Smith), organizing
of lifestyle marketing and the power of content customization,
content around expressed preferences, etc.
however, this will rapidly change. It is important to understand
4 that targeting and relevancy are not the same thing. With Recommendations – focuses on displaying lists of products
targeting, it’s easy enough to say a certain group of people based on a predictive algorithm. Netflix and Amazon are the two
(say… New England Patriots fans) should be included in a
5 best known examples. Display can be implicit, i.e., which items
campaign, but which item to promote (from the Tom Brady appear when on the home page, or explicit as when likely lists
game jersey to a Belichick hoodie) could be tied to past displayed with an item or promoted via an email.
6 purchase history.
Relevancy – spans into the content spectrum and covers
The opportunity here however isn’t just about product both personalization and recommendations. Also, uses same
recommendations. This approach can as easily be applied
7 to an email subject line, salutation on a mailed piece, display
algorithmic type approach used in recommendations and
Blackbox Analytics.
media placement, or virtually any other form of communication.
8 Something that has held this area back is its dependency on
data and analytics. Folks started dabbling with algorithmic black
Relevancy and Targeting are not the same thing
boxes in the late 90’s, but they proved unsuccessful because
9 they suffered from a lack of data or the scalable computing
power to handle extremely large data sets. Today, these are
two problems that have been solved. Over the next two years
10 look to this area as key point of innovation. The masses are Vendors
clamoring for relevant communications, and technology will be
central to delivering it.
11

12

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traditional-
Acronyms / Terms:
1 Database Marketing – the use of personal information to

2 DIRECT segment customers and/or prospects in order to personalize
marketing communication to individuals.

List Processing / Data Hygiene – analysis of list files to ensure
Going back to the days of the Pony Express, direct marketing accuracy of names and addresses, remove duplicate entries,
3 has been one of the most profitable and attributable forms etc. This is important for management of mailing costs and
of marketing. A large reason for this is that it is also one of maximizing deliverability.
the most trackable. Nearly everything around circulation
4 National Change of Address (NCOA) – an estimated 8% of
is measurable, including sends, suppressions, test and
mail is undeliverable because the intended recipient has moved.
control, hold outs, key codes, QR codes, and so on. Over
NCOA systems make changes of address data available to direct
time, marketers have fine tuned a number of approaches to
5 managing their campaign spends and list sizes to maximize
marketers to ensure mail is being sent to recipient’s current
location.
response.
ZIP +4 – 4 digits are appended to the end of standard ZIP Codes
6 Now, we think that postal and circulation are sun setting.
to provide even more geographic specificity to simplify sorting and
Whether it’s 5, 10, 15, or 25 years out, there is a future in
delivery of mail to high-volume areas. The Postal Service will give
front of us that likely will not include direct mail. What is
7 unfortunately frequently missed in this scenario, however, is
a lower postage rate for mass mailers adding the +4.
that the skill sets developed here are still enormously relevant QR Codes – encoded UPC code, that resembles a pixilated black
to marketing. In fact, marketers in digital fields are now starting and white stamp, that can be scanned in from a mobile devices
8 to worry about using multivariate or split testing, looking at camera that tracks and redirects a response to a website.
conversion optimization, and finding ways to measure multi-
channel response. These are things direct marketers have
9 struggled with and have developed and fine-tuned various
approaches to deal with them. The biggest challenge we face Vendors: MARKETING SERVICE PROVIDER
as marketers is overcoming our internal biases to leverage
10 what works best across the digital and offline disciplines to
solve today’s - and the future’s - marketing problems.
11 Vendors: CAMPAIGN MANAGEMENT

12

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traditional-
Acronyms / Terms:
1 In-Store – includes digitally-enabled interaction with mobile

2 experience devices and in-store video monitors. Merchandising plannograms
and signage are also part of the mix.

Point of Sale (POS) – where on-demand coupons are printed,
A customer’s brand experience involves more than receiving loyalty programs are offered and participation is tracked.
3 a campaign through the mail or via email. When people are
ready to buy or otherwise interact with a brand they still regularly
visit brick-and-mortar store locations, attend events, or even
4 reach out to a call center representative. These encounters can Vendors: EVENTS
become foundational to attitude and opinions about the brand.

5 It’s also critical from a marketing standpoint to gather as
much information as possible while directly engaged with Vendors: IN-STORE
the customer. In retail, for example, it’s important to capture
6 customer info at the Point of Sale or POS. Whether through a
loyalty program, sweepstake or FSI, there are a lot of ways to
capture this information. The best ones, typically rendered via a
7 digital POS, will also have a verification feature during address
Vendors: POINT OF SALE
capture to ensure the information received is accurate and
deliverable.
8
Although the client / guest / member experience sometimes
Vendors: CALL CENTER
rolls up to individuals outside of marketing, it is still important for
9 marketers to keep this in mind. There are also a growing number
of ways that marketers can impact the customer’s experience
near the bottom of the funnel. One of the areas that is most
10 developed and most tapped into from a marketing standpoint is
the call center. For both inbound and outbound (telemarketing)
calls, past transaction history and CRM data can be used to
11 generate up-sell and cross-sell scripts in real-time, during the
call. Overall, influencing the customer experience is a largely
untapped area where a lot of new solutions, the most compelling
12 being mobile, are starting to gain traction.

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traditional-
Sponsorship – lending support, usually financial, to places,
events or organizations in exchange for brand recognition (which
1

image
differentiates sponsorship from philanthropy). Sponsorship can
demonstrate a brand’s core values, strengthen its reputation and
increase exposure to the public.
2
Cause Marketing – related to sponsorship. A for-profit brand
When forming brand perception, arguably the most important associates itself with a “good cause” by giving monetary or other
3 impression is the first one. Frequently, even in the days before support to non-profit or charitable organizations.
social media, this first touch often could come from a 3rd party
and not the brand itself. Influencing, as opposed to controlling,
4 these 3rd parties is the parlance of public relations.

Today, the power and reach of image perception has been
5 totally altered because of social media. It has exploded in
tions
blic rela ond
two contexts - in terms of persuasive reach, but also in the
Pu
r bey
structure and approach to distributing the message. In the old
6 world, PR could focus on a few key media outlets. Now, they
goes fa ional print
it
the trad ements of
still need to serve the main outlets, but must also address a
ac
media pl into active
much wider, fragmented graph of social influencers.
7
t
the pas t with
While the majority of PR efforts are still human ones, an area
en
engagem nd industry
that technology is impacting the space is in social monitoring.
8 Some of the most noted applications for this involve crisis
a
management, but it’s also really important for identifying key bloggers s.
er
9
influencers for micro-small scale outreach.
influenc

Acronyms / Terms:
10
Placement – the strategic positioning of product, logos, etc.,
within media (generally movies and television shows, but more
11 Vendors
and more in video games). This concept is used to embed
advertisements directly into non-commercial contexts. Became
popular in the 1980’s.
12

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traditional-
digital service it gives advertisers the ability to deliver a
tailored, personalized message to a known audience at a
1

media
specific address. That means that two different neighbors may
be watching the same program, but seeing different ads.

2
The king is dead! Long live the king! While no doubt, all Vendors: print
3 traditional broadcast media has struggled and lost ground
to digital counterparts, collectively it still represents the vast
majority of overall marketing spend. This is not by chance. The
4 dollars remain because so does the most important factor –
audience. If you are looking for reach, mass media, television Vendors: television
in particular, offers the most efficient way to reach a broad
5 audience quickly. This is not to say, however, the space isn’t
evolving. Rapidly you see the medium of television melding with
digital video counterparts. Whether streaming, video on demand
6 delivered via the web or mobile device, or even addressable set Vendors: addressable tv
tops, you can see a day where traditional will closely mirror, or
just serve as an extension of other digital strategies.
7
Acronyms / Terms: Vendors: radio
8
Addressable Television Advertising – using techniques
similar to those utilized in internet marketing, data can
9 be collected to target ads per viewer, rather than placing
commercials in the context of the channel, show and time of day.

10 Pod – commercial time segment during television shows.

Slot – subdivision of a pod.
11 Sweeps – period of time in which Nielsen collects data from
television viewers to rank channels and shows.
12 Digital Television – as more and more homes move towards

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digital-
Acronyms / Terms:
1 Advertising Networks – companies that broker relationships

2 DISPLAY between advertisers looking for an audience and website
owners that want to sell advertising space.

Contextual Advertising – 3rd-party advertisements which
There is no doubt that digital display advertising has become match the context of the content of the webpage on which they
3 a critical part of the media mix and a central component of appear. They are used to target prospects based on the topics
many acquisition strategies. And while overall display spends they are actively engaging with.
are still dwarfed by traditional television counterparts, that has
4 not stopped the likes of Microsoft and Google from investing Behavioral Based Advertising –3rd-party advertisements
billions in ad serving platforms like Atlas and DoubleClick. which are tailored to particular users. Service providers track
users’ online behavior (such as sites visited), analyze data
5 The prevalent theme today in the display space is the and target ads based on the information collected. Involves
movement and momentum towards the various flavors of massive data collection and causes many privacy concerns.
targeting. What began with contextual, or showing ads that
Banner sizes – a few examples of standard ad units, include:
6 match the content of the site, moved to behavioral, where click
stream history or past sites visited drove the decision, and has -- Leaderboard – 728x90
now evolved to even addressable audiences where content -- Medium Rectangle – 320x250
7 can be targeted to specific audience members. The motivation
for all of which is the ability to show a more relevant ad to the -- Rectangle – 180x150
right person thereby maximizing the likelihood of attracting -- Skyscraper – 120x600
8 a click.
-- Wide Skyscraper – 160x600
Behind the scenes of all of this is an incredible ecosystem
9 of technology, data and service providers that bring this all
together. Penetrating the display space is challenging for the
uninitiated, but there is a lot to be gained from understanding Vendors: AD services
10 the inner workings. If there was a single space that mirrors the
future state of how a data-driven marketing organization will
operate – it’s display.
11 Vendors: Data Management Platforms

12
for a more comprehensive list of vendors in this space, click here: LUMA Partner report

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1 digital-
2 SEARCH
In the marketing world, search is a critical channel that isn’t
3 always maximized. Those who blow budgets buying up
keywords and focusing on search rankings are really missing
the opportunity that search presents. Unlike other forms of
4 mass advertising such as display or syndication, with search
the impression was initiated by a customer seeking out and
then engaging with you. Right away, you know this customer is
5 likely past awareness into the interest phase.

Very few companies, however, seem able to recognize and
6 respond to this fact. It’s not a technological limitation, the
technology can do it, it’s that marketers compartmentalize their
efforts by channel as opposed to thinking like a sales person
7 who needs to follow a prospect from first touch all the way
through to sale.

8 An example of sophisticated search use comes from an
Experian client who is an electronics retailer. Experian used
search intelligence to help the client determine (ahead of its
9 competitors) which products and brands “early adopters” were
searching for. These insights became a “leading indicator”
of sales in the near future. If there is a glimmer of hope that
10 marketers are starting to get wise to this, it can be found in
the school of thought around “inbound marketing.” Here, you
see marketers starting to invest in understanding how SEO,
11 versus PPC, can be used in combination with other channels
to effectively drive acquisition.
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Acronyms / Terms:
1 Search Engine Marketing (SEM) – web strategy which
includes aggregate of all paid and organic methods to reach a
target audience via search engine inquiries.
2
still
ters are new
Search Engine Optimization (SEO) – the ongoing process of
Marke
g with
increasing a website’s visibility to search engines using unpaid
3 means.
innovatin f online
o
Linkbuilding /Backlink Strategy – actively seeking to obtain aspects hich has a
w
4 inbound links from other sites for the purpose of improving
search, uence
search engine ranking.
powe rful infl
ntire
Pay Per Click (PPC) Advertising – text and image on the e journey.
5 er
custom
advertisements positioned by vendors which advertisers only
pay for when a visitor clicks on it.
6 Vertical Search – search engines which focus on a specific
segment of online content.

7 Black Hat – using shady techniques to game the search
engines to promote search rankings.

8 Bounce – visitors that land on the site but quickly leave Vendors: search engines
without engaging.

Inbound Marketing – school of thought fathered by folks at
9
HubSpot that teaches the virtues of combining organic search
with social syndication and community, as well as a content Vendors: search tools
10 creation strategy to improve acquisition.

11
Vendors: inbound marketing

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digital-
Vanity URL – a custom web address that connotes the content
the user will see.
1

WEB
Microsite – a very thin site, tied to a vanity URL that is tailored
to a very specific audience or offering.
2 Conversion Optimization – is an emerging field of
While the technology of websites has cetainly evolved specialization around how you can maximize the number of
3 dramatically over the years, from static brochure-ware to registrants, or purchases, or whatever your critical metric is,
dynamic, media-rich “experiences,” from a marketing standpoint you get from your site visitors.
the main transformation has been felt in the website’s changing
4 role in the sales process. Once upon a time, the website
represented a brand’s digital footprint and thus had to address,
as well as it could, the entire buying process. Today, however,
5 you now see that being broken up by channel.

Using a sales funnel like A.I.D.A (awareness, interest, desire,
6 acquisition) as reference, the focus of websites, or tailored
microsites, are emerging as mid-funnel and down. You don’t
use a site to drive awareness, you use it to nurture interest
7 and drive acquisition / capture. A lot of attention is now being
paid to how the website can be used as a net to capture initial
awareness created through other digital, social and even
8 traditional channels.

9 Acronyms / Terms:
Web Content Management or Content Management System
10 (WCM or CMS) – refers to a class of software that provides
workflows that enable users to develop, manage and update
digital content on a webpage. Vendors
11
Pathway – the manner in which a visitor navigates website
content. Pathways can and should be designed purposefully
12 with specific audiences in mind.

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digital-
Acronyms / Terms:
1 Spam – unsolicited email messages, usually delivered in bulk.

2 EMAIL Open – when a recipient views an email. Open is detected
by embedded HTML content. Use of opens as a metric is
declining due to image blockers which disable the capability of
Is email a perfect medium? Some would argue that it is. You tracking opens.
3 can deliver a rich message and target content tailored to an
Click – when a user clicks on a link within an email. Clicks are
addressable audience – all at a marginal cost.
an increasingly popular gauge of the effectiveness of email
4 Over the years, the focus of email has shifted from acquisition messages.
to retention. In the early days of email, when practices mirrored
those of direct marketing brethren, it was largely used as an Multivariate Testing – measuring the effectiveness of multiple
aspects (subject line/headline, body copy, images, etc.) of a
5 acquisition vehicle. As such, it became common practice to
marketing message at the same time.
acquire lists of addresses by whatever means possible to drive
greater impressions. The math was pretty simple – add more

6 addresses to the top and you’re guaranteed greater revenue at
the bottom. Over the years these batch and blast strategies, hit
Vendors: EMAIL SERVICE PROVIDERS
a saturation point and have come to realize dwindling rates of
7 return.

Today, email has evolved from prospecting and acquisition to
8 nurturing and retention. It’s no longer about just growing list
size; it’s about being able to deliver the most relevant content
possible. While incremental costs for each additional send are
Vendors: MARKET AUTOMATION
9 approaching zero, email marketers that understand how email is
evolving will no longer just measure cost of campaigns in dollars
and cents, but in a currency of customer-attention span. If
10 marketers continue to spam customers with unwanted, untimely
messages they risk being totally tuned out when the time for the
right message comes.
11

12

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mobile-
As devices have matured so too have the email tools. Today’s
best email tools must be able to support multiple versions of an
1

DIRECT
email for each targeted platform.

The final direct messaging format to discuss is alerts. Basically

MARKETING
2 they are old school Wall type of posts that are pushed to
the device and pop up in the middle of the screen. If you’ve
downloaded one of the popular weather or banking apps
3 you’ve likely received an alert informing you of an impending
There are a lot of ways to deliver a direct message to a mobile thunderstorm or deposit verification. While alerts are pretty new,
device. They all have varying use in the customer lifecycle and much like texting, the likely marketing application for these will be
4 can be dictated by platform. Let’s address three of the most mid-funnel and down.
common: SMS/MMS texting, email, and alerts. “We want to While today there are multiple vehicles for delivering direct
5 hear from you America!” implores Ryan Seacrest. If you’ve messages to a mobile device there is going to be consolidation
watched American Idol over the last decade and indulged your in this space. The technology behind pushing a message is
guilty pleasure of voting for the likes of Sanjya, you’re familiar shared across all technologies. As marketers begin the shift from
6 with one of the most common forms of direct mobile messaging channel optimization to customer optimization this will be an easy
– SMS texting. Here, often, the marketer’s aim is to entice an integration point.
audience to engage with the brand by texting a special code to
7 a specific number.
Acronyms / Terms:
Voting, competitions, balance updates, and FAQ requests
8 cover the most common uses. Note here, that all of these are Short Messaging Service (SMS) / Multimedia Messaging
mid-funnel type activities. Service (MMS) – whether sending 160 character texts (SMS),
or snapshots taken from the phone (MMS), these are the
Because of their shared heritage, the next option to address
9 is mobile email. This is an area where there has been a lot of
technologies that enable directly sending non-audio content
between two devices. Technically speaking SMS/MMS closely
change over the decade. For example, the advent of digital
resemble email. Most, if not all, of today’s sophisticated email
10 content delivery to phones began with brick-like BlackBerry
platforms are capable (natively or through partnership) of blasting
devices. These early versions couldn’t render HTML content
a text (1-way), or responding to a text-in code (2-way).
and only could handle very simple block print. They were great
11 for business communications, but limiting to marketers. To help
remedy this, email tools began to offer solutions that were able Vendors
to detect the rendering device and offer targeted versioning.
12

13

BIO

TOC

mobile-
Acronyms / Terms:
1 Wireless Application Protocol (WAP) – what HTTP is

2 web to websites, WAP is to mobile. This is the key technology
component to enable the transfer of content to phones.

Location Based Services – all sorts of application providers
Nowhere is the ubiquity of digital more evident than the have started tapping into the power of universally available GPS
3 evolution of mobile devices. Just five years ago it would be (Global Positioning System) data. Whether push (beaming a
most common and correct to refer to the device you carried message at someone) or pull (responding to a search) we are just
as your mobile or cell phone. Today the device we carry still beginning to tap into the potential here.
4 serves the purpose of making phone calls, but as we and our
customers know it does so much more. Mobile web doesn’t
correlate exactly to digital web. While yes, it embodies the
5 translation of full size HTML websites to micro-screen WAP
ones, it also needs to address technology and interface
is the
owhere
differences.
6 N ital
For example, location based services offer a totally new way
ubiquit y of dig
han
evident t
to conduct a search. Looking for the closest gastro-pub or late

more
7 night taco stand has never been so easy. Mobile opens up a
n of
evolutio
whole new way to how we search for and consume information.
the
vices.
Savvy marketers will understand it’s not just about being

obile de
8 present in mobile, but “localizing” the experience to optimize it
for the device and customer’s need. m
9

10
Vendors
11

12

13

BIO

TOC

mobile-
the connectedness of deep data knowledge and availability of
multiple versions of content.
1

2 APPS Acronyms / Terms:
App Marketplace – is the open marketplace where you upload
Eric Qualman, author of Socialnomics, produced an excellent your application. Depending on platform (Apple or Android) there
3 chorological timeline of the adoption of new media channels. is a licensing and approval process associated with placing your
He notes that it took TV a couple of decades to reach app in the marketplace.
something like 50M viewers, Facebook a couple of years to
4 Tablet – okay, maybe you spent the last two years vacationing
reach 700M, but only nine months for the apps marketplace to
on your private island. The tablet space, dominated by Apple’s
reach 1B downloads. As the fastest-adopted channel, this has
iPad, covers the notebook sized and finger swipe touch pad in a
to be on your radar. The app marketplace is still in its infancy,
5 but from a marketer’s standpoint there are two early formats
reasonably priced packages of goodness.
that are working best – gaming and commerce. Catalog Apps - many print catalogs and mailers are being
converted to downloadable apps to reach new customers, avoid
6 The casual gaming market is explosive. Not just for addictive
printing and mailing costs where possible, improve retention and
puzzle games like Angry Birds, but also for advertising
increase basket size.
sponsored games. The approach here is to provide an
7 engaging, addictive experience that captures the audience’s
attention while reserving real estate for advertising. This is
the most common advertising usage of the apps, but you will Vendors: Commerce Apps
8 also see “placement” as you do with commercial products in
movies.

9 Some pundits speak to email as a near perfect vehicle for
individual communications. Similarly, there is emerging talk
about the potential of tablets as a couch-based commerce
10 Vendors: Apps Add exchange
device that will rival the catalog businesses of yore. From
simple static-ware catalog ports to interactive store fronts,
there are a lot of flavors being tested in market right now.
11 Clearly though, where this is headed is to a new experience
that maximizes the potential of the tablet. Look for new “stores”
that leverage the intuitiveness of the interface, the interactivity
12 of onboard cameras, the comfortable mobility of location, and

13

BIO

TOC

mobile-
had scanning stations in the store to report product price. Wonder
where they can take that now? Load your Target app on your
1

scanning
phone and it gives you the full litany on the item including social
reviews. There is going to be a lot of competition to own the in-
store market for considered purchase items in particular. You can
2 also see how CPG’s and other food producers will want to push
the best face of their products forward.
When the first cell phone camera was released in Japan
3 by J-Phone (now called SoftBank Mobile) and the Sharp
Corporation in November 2000, you have to wonder if they Acronyms / Terms:
ever imagined where the technology could go. It probably
4 would have been much closer to the wildly popular “purikura” Quick Response Code (QR) – the square-shaped, barcode-like
photo booths as opposed to a replacement for a beamed symbols that are popping up all over the place. Principally, they
laser scanner! While there are a few different ways this is are used to replace a written URL address. Why ask a consumer
5 being implemented, the common aim can be expressed as to type in http://www.experian.com/business-services/customer-
trying to bring more information to the audience faster. For data- management.html when they can just scan in a simple
example, this can come in the form of specialty codes that symbol?
6 lookup websites, or reading of product UPC codes to provide
Near Field Communication (NFC) – as the name suggests, it’s
competitive pricing or nutritional value. Let’s look at a couple of
a form of communication whereby a device (like a phone) can
these in more detail.
7 emit a signal that is picked up in a proximity measured in inches.
One of the limitations of mobile devices is their teensy Near term applications? Forget swiping your credit card, just
keyboard. The thumb speed of Millennials notwithstanding, the bump your phone to the payment pad.
8 reality is they aren’t great devices for typing in long, precise
addresses that can’t be auto completed or short keyed. What
generally all mobile devices now have – a camera, offers a Vendors
9 solution to this problem. Instead of having somone type in an
address, you can have them take a picture of a QR Code that
links to it. When scanning in these special images it becomes
10 an easy way to draw in an audience.

Another very interesting application is an in-store barcode
11 reader. It’s not just the capturing of the barcode, but all the
information that can be provided off of it. Today, innovators like
RedLaser are using it to provide competitive information on
12 pricing by product or retailer. Retailers like Target have long

13

BIO

TOC

1 SOCIAL-WOM
(Paid Community
2 Engagement)
Discussion around a brand largely takes place outside of
3 official marketing campaigns. Consumers are eager to share
their experiences with their peers. Word of mouth (WOM) has
always been a dominant factor in the forming of consumer
4 opinion, but the proliferation of social media has both
accelerated and multiplied its impact.
5 Consumers today are more likely to voice their views of a
product or service - and they can speak to a larger audience
via social networks. One way to tap into this trend is by
6 sponsoring an advocate with a social influence to champion
the brand. This approach is most associated with the “mommy
blogger” working for product samples or minor payment. From
7 a marketing standpoint, you could also consider a WOM
campaign to be in the same vein a sponsoring an E-list (as
8 opposed to A) celebrity, or even as pay-per-click (PPC) search
(versus organic).

9
Acronyms / Terms:
Word of Mouth Marketing (WOMM) – leveraging the power
10 of social networking to gain influence in the word of mouth
arena. This involves purposefully inspiring conversation
about a brand, product or service. Individuals can be given
11 Vendors
opportunity to experience the brand for review, then spread
that experience to their networks. WOMM is effective in that it
12 is organic (i.e. peer-to-peer) rather than B2C marketing (often
seen as “manufactured” and biased).

13

BIO

TOC

SOCIAL-
Acronyms / Terms:
1 Blog – used to publish updates, opinions, information,

2 COMMUNITY
(ORGANIC / AUTHENTIC
editorial content, etc., with varying degrees of frequency, to be
consumed by community members or the general public.

Wall – similar to a blog, but in reverse. It is a space on user’s
profile page that allows friends to post messages for the user
3 ENGAGEMENT) to see. Think Facebook.

A community engagement strategy is really a catchall for many Message Board or Forum – an online discussion site where
4 different things. In some cases, it can reference the creation conversations can be held in the form of posted messages.
and sponsorship of a captive community, like you’d see around
Oprah’s O Network, or a special interest group, like the local
5 little league team’s parents. Or, it can speak to a brand’s Vendors: SOCIAL NETWORKS
community engagement strategy whereby spokespeople, ideally
actual employees, engage in conversations on relevant topics or
6 with targeted audiences across the social sphere. Blogging, link
sharing, friending, liking, stumbling upon, etc. are all examples
of the ways brands can engage. Vendors: COMMUNITY PLATFORMS
7
If there is a unifying theme to community engagement it’s
around extending the customer relationship and making the
8 brand relevant past the point of purchase. A lot of relationship
marketing is focused on evolving the perception of the brand
from a product choice to a lifestyle one. It’s not just about selling Vendors: COMMUNITY Software
9 the next tube of lipstick, it’s about owning a part of the wallet
share and psyche of how the customer defines herself.
10 A big success component in organic community development is
authenticity. When you are trying to reach people on a personal
level, it’s very difficult to feign interest or commitment to shared
11 ideals. You need to commit to your customer and audience.

12

13

BIO

TOC

SOCIAL-
Acronyms / Terms:
1 Social Media Monitoring (SMM) – harvesting comments,

2 MONITORING posts, tweets and so on, from across the spectrum of social
media sites wherever public contributions are accepted.

One of the biggest paradigm shifts that social brings to
3 marketing is the loss of control around brand message. In the
past, brands were basically the sole creator of content, but in
dia
ocial me
today’s world the customer has a participatory role and creates,
4 S
ing
monitor ands
shares and spreads the message.

br
enables
Social media monitoring enables brands to tap into the social
the
ap into
5 voice of the customer. All the comments, posts, reviews,
spanning social networks, news sites, blogging platforms,
to t
the
voice of .
community sites, etc., are matters of public record that can be
6 er
custom
captured and recorded.

Functioning similarly to the back end of search engine
7 technology like Google, the monitoring tools harvest
conversations from across the social web.

The marketing application of this data is varied. A typical and Vendors
8 critical application of social media monitoring is for handling
crisis management. When something negative is going down
9 about the brand, it’s really important to be proactive and
engaged with the community to address and rebut unwanted
sentiment. Another scenario is utilizing social as the world’s
10 largest virtual ethnography to drive new innovations around
media mix modeling, consumer insights and even product
strategy.
Small sidebar: Monitoring is extremely limited on Facebook.
11 Individual fan pages can be wired up to be harvested, but they
Important to note, unless a site’s security policies lock down can’t programmatically crawl individuals’ pages. While not
content (such as Facebook), all the public comments are a
12 matter of public record.
accessible to SMM tools, FB does expose quite a bit of data about
fan pages and public profiles through its open API.

13

BIO

TOC

SOCIAL-
Acronyms / Terms:
1 Syndication – repurposing or republishing original content on

2 syndication third-party sources.

Hyperblog / Microblog – an online medium for broadcasting
short messages either for specific recipients or for the public.
The concept of syndication is well established in the media Think Twitter.
3 world. From reruns of old Seinfeld episodes on local TV
Viral – from a marketing standpoint it references creating
channels, to reprints of Associated Press stories across
newspapers and web outlets, content is regularly being something that is liked by your targeted audience and set free
4 shared, repackaged and reproduced across multiple platforms. for them to share wide and far as they see fit. It’s letting the
power of the crowd do its work.
With traditional syndication, the content producer is
5 compensated as the publisher (e.g., ABC, CBS, Fox, etc.)
uses the content to attract and retain an audience. And in
turn the publisher can use the audience to attract advertising Vendors: SITES
6 dollars from advertisers.

Social media, however, poses a whole new model for content
7 syndication. With the emergence of multiple independent
networks (e.g. YouTube, SlideShare, Scribd, Twitter, etc.)
content creators and advertisers can now reach audiences
8 directly without the aid of publishers. In this model, content can
be distributed and a following can be generated for no more Vendors: publishing tools
than the cost of content production. It’s all about creating the
9 next “viral” content.

A familiar approach for describing this shift can be captured in
10 terms of the P.O.E. (Paid, Owned, Earned) model. Classically,
the “paid” portion has been the dominant share of media
exposure. Really, all social hype aside, it still is. Its dominance,
11 however, is certainly eroding with digital driving “owned”
properties, and social driving “earned.”

12

13

BIO

TOC

1 BIOs
2 Marcus Tewksbury: Author
Marcus Tewksbury is a product strategy and business development expert with over 15 years of experience defining, marketing,
3 and ultimately selling new B2B marketing services and technology offerings.

Today, Marcus focuses on strategic accounts for Experian Marketing Services, a powerful new agency model focused on driving
relevant messaging based on sound customer intelligence, where he partners with marketing executives on how to best harness
4
their customer relationships to develop “big ideas” that open new markets and expands revenue opportunities with existing ones.

http://twitter.com/tewksbum http://www.linkedin.com/in/tewksbum marcus.tewksbury@experian.com
5

6 Andy Roy: Contributor
Andy is a digital marketing strategist with Experian Marketing Services. He brings 25 years of experience in product innovation,
ecommerce, business intelligence, and sales enablement to the challenges of marketing in the age of the digital consumer. His
7 areas of interest include tablet shopping, personalization, mobile apps and loyalty marketing.

http://twitter.com/anindaroy http://www.linkedin.com/in/anindaroy andy.roy@experian.com
8

9 http://www.experian.com/business-services/customer-data-management.html

10
Experian Marketing Services helps companies target and engage their best customers through integrated email, mobile and
social media marketing programs; digital advertising; data management; customer and competitive insight; and analytics and
11 strategic consulting. Through these diverse capabilities, our clients enhance brand advocacy, create measurable return on
marketing investment and significantly improve the lifetime value of their customers.
12 The intention of this document is to present a map and a framework. It highlights and calls out vendors that are representative of the space. These listings, by no means, should
be considered comprehensive. If a vendor is omitted, please contact the author for evaluation into the next innovation report.

13

BIO

Al Bessin has provided leadership for retail and direct marketing businesses at
strategic and tactical levels since 1983. At Merkle, Al oversees the Delivery
Team team in the Specialty Retail group, which provides marketing services,
manages production, and supports Merkle’s Specialty Retail marketing solution.

Al joined Merkle in 2011, when the consulting firm he was with, where he served
as a partner for six years, was acquired. Al provided consulting in the areas of
strategic planning, internet and catalog marketing, and management
development to multichannel retailers. He provided services to many well-known
retail brands, such as Performance Bicycle, Utrecht Art Supply, Bass Pro Shops,
and JC Whitney, as well as niche retailers.

Al has worked in every aspect of ecommerce, catalog and retail operations, both
at strategic and hands-on levels. He has experience with small- and medium-
sized start-ups, high growth companies, and turnarounds, both nationally and
internationally. He has also worked with the investment banking community on
several mergers and acquisitions.

Previously, Al served as co-CEO for The GolfWorks, a multichannel retailer and
wholesaler of golf clubmaking and repair products.

Prior to that he was the COO for Musician’s Friend, the world’s largest music
gear internet and catalog retailer. He also served as vice president at Golfsmith,
a golf equipment multichannel retailer, and worked on strategic planning for
Apple Computer’s chain-store and independent dealers.

Al has an MBA from SMU and a BA from the University of California at Berkeley.

As a recognized expert in retail and direct marketing, he has spoken at many
industry conferences, investment banking conferences, and at user group
meetings for order management software companies.

9/18/2012

Leveraging Your Database:
Reporting, Templates & Strategic Applications

Al Bessin
Vice President, Specialty Retail
Merkle
abessin@merkleinc.com
512.745.9070

The Big Points
• Background
• Who is My Customer?
– Starting with Good Data
• Customer Balance Sheet
– Sample Reports
• Media Reporting
– Resolving Competing Media
– Attribution and Testing
• Performance Measures
– Contribution Analysis and Customer Value
– Media and Campaign Analysis
• Putting It All Together
• Q&A

1

9/18/2012

Premise: Marketing Database is Just A Tool
• Need a Platform to support marketing
– Objectives
– Planning
– Execution
– Analysis

Goal: To Maximize Customer Value
• Relevant & timely communication increases value
• Understand customer purchasing velocity
• Proactively adjust your marketing and message
Target Acquire Develop Retain Grow

Dialogue Optimized

High
Up‐Sell

First Purchase
Customer Value

Average

Low

Time

2

9/18/2012

Identifying the Customer

Starting with a Clean Customer File
• Reconciling to a unique customer is key to accurate analysis

3

9/18/2012

Customer Balance Sheet
• Measuring changes in customer mix is an essential exercise
• Take snapshots on a quarterly basis to account for seasonality

Customer Balance Sheet Reporting
• Summarize changes in customer file composition for better view of trends
• Combine the Balance Sheet with the “Income Statement” or view of what
happened in the period

4

9/18/2012

Customer File Views
• Use views that support your business trends and objectives

2009 2010 2011 First Order Year
Last Order 2006 2007 2008 2009 2010 2011 Total
Q1 210,926 233,993 259,835 2006 86,692 - - - - - 86,692
New 23,278 24,067 25,263 2007 16,615 72,524 - - - - 89,139
Reactivated 10,751 12,372 13,545 2008 15,344 14,111 65,794 - - - 95,249
Retained 176,897 197,554 221,027 2009 17,094 13,616 14,059 73,842 - - 118,611
2010 23,647 16,380 14,822 19,017 94,520 - 168,386
Q2 213,272 231,955 259,211 2011 41,249 22,889 18,767 20,790 29,011 91,927 224,633
New 18,808 16,815 16,260 Total 200,641 139,520 113,442 113,649 123,531 91,927 782,710
Reactivated 7,657 8,653 9,225
Retained 186,807 206,487 233,726
Q3 214,355 235,929 256,320
New 14,374 17,568 15,486
Reactivated 9,002 9,996 9,854
Retained 190,979 208,365 230,980
Q4 229,730 255,929 -
New 54,041 62,980 -
Reactivated 21,169 24,637 -
Retained 154,520 168,312 -

Media Reporting

5

9/18/2012

The Challenge
• Number and types of marketing media have exploded
• Consumer behavior continues to evolve Social
Marketing

? Catalogs

• Campaign analysis is increasingly complex
Actual
• Intense competition for marketing $s Total
Mass
Demand
Media
Email

• Results are overstated CSEs

Organic
Search

Paid
Search

Attribution Methodologies
• Vendor reporting alone does not work
• Matchback to contact files
– Assumes all demand within windows is driven by the one contact
– Only factors in push campaigns
• Web order referring source (last touch)
– Credits only the incoming medium – may simply be convenient
• Last touch online plus matchback
– Simple to implement, normalizes total demand
– Can use weighting and fractional allocation
• Multi‐touch attribution
– Very complex, but still a model
– Perils of cumulative errors for smaller populations

6

9/18/2012

Resolve Demand Across Media
• Most important – develop a holistic view of demand across all media
• Start simply, if necessary, and then evolve

Order Management Feed Allocation Model Output
Reporting Channel Demand Allocated Demand by Medium Demand
Catalog $371,587 Catalog $527,742
Website $322,694 Email $108,128
Amazon $18,245 Paid Search ‐ Brand $7,843
Email $89,435 Paid Search ‐ Competitive $45,889
Total $801,961 Natural Search ‐ Brand $8,457
Resolve Natural Search ‐ Competitive $86,384
Comparison Shopping Engines $0
Google Analytics Feed Marketplaces $17,518
Allocated Demand by Medium Demand Total $801,961
Paid Search $85,971
Natural Search $134,674
Comparison Shopping Engine $0
Other $191,484
Total $412,129

Strive for a holistic view of marketing

Test to Determine Validity
• For email, direct mail and telemarketing, holdout campaigns are ideal tests
– Simple – measure total purchases by each population
– Sometimes management resists

Tips:
• Test over sufficient time
• Hold test groups constant
• Ensure sample size is sufficient to get statistical significance

7

9/18/2012

Performance Measures

Background Overview of Financial Metrics
• Financial Metrics
– Sales
– Gross Margin (differentiate between product margins and gross margin)
– S&H Contribution (Expense)
– Variable Transaction Expenses
– Marketing Expenses
– Semi Variable Expenses
– Fixed Expenses

• Contribution Analysis
– Typically to make incremental decisions
• Solve for the cost to drive n+1 revenue
– Relevant expenses are variable

8

9/18/2012

Defining Order Contribution
• Order contribution analysis is critical to evaluating marketing effectiveness
• May require data external to the marketing database to complete

Marketing Contribution and Breakeven
• Solve for the demand needed to cover variable marketing expense
– Catalog/Direct Mail/Email examples are simplest

9

9/18/2012

Customer Value Analysis
• Defining “Lifetime”
– Financial ROI windows are typically one to two years
– Buyer behavior typically falls off rapidly after a few years
• Application of Value Analysis
– Establish metric for acquisition cost
– Basis to compare different media and quality of acquisitions

“Lifetime Value” is the variable contribution in the first and second years
after initial purchase

• Lifetime Value = Sum of Order Gross Margin less Variable Transaction
Expense less Marketing Expense for orders in a one‐ or tw0‐year window
after the initial order

Example: Traditional vs. Digital Media LTV

10

9/18/2012

Media Evaluation
• Evaluate marketing programs based on
– Cost of new buyer account acquisition
– Comparative value of acquired buyers
– Contribution across retained buyers
– Marketing ROI

• Tailor for maximum effectiveness for each target group
– Contact type
– Contact cadence

Media Performance
• Report on Media rather than by Order Method
– It is much more relevant for marketing (and more predictive)

Demand by Promotional Media
Promotional Media Month TY Month LY ∆% TY/LY
Allocated Demand by Marke ng Medium
$2,500,000 Catalog $1,282,145 $1,155,720 10.9%
Natural Search ‐ Branded $124,778 $41,710 199.2%
$2,000,000 Natural Search ‐ Non Branded $58,486 $14,586 301.0%
Paid Search ‐ Branded $52,313 $0
$1,500,000
Paid Search ‐ Non Branded $26,032 $200 12915.9%
$1,000,000 Web (Other) $277,457 $307,173 ‐9.7%
Email $179,419 $114,522 56.7%
$500,000 Grand Total $2,000,630 $1,633,911 22.4%
$0
Catalog Natural Natural Paid SearchPaid Search Web Email Grand
Search ‐ Search ‐ ‐ Branded ‐ Non (Other) Total
Branded Non Branded
Branded

Month TY Month LY

11

9/18/2012

Acquisition Costs by Marketing Media
• Cost per acquired new buyer is part of the equation

TY 1st Order LY 1st Order TY New LY New
Contribution Contribution Buyers Buyers #% TY/LY ∆% TY/LY
Catalog $ (4.32) $ (4.75) 2,345 2,568 (223) ‐9%
Email $ 14.20 $ 16.19 212 84 128 152%
SEM ‐ Branded $ 8.56 $ 10.02 217 21 196 933%
SEM ‐ Competit $ (1.31) $ (0.05) 648 500 148 30%
SEO ‐ Branded $ 8.56 $ 10.86 213 123 90 73%
SEO ‐ Competit $ 10.31 $ 10.21 875 722 153 21%
Comparison Sh $ 6.32 $ 7.52 236 ‐ 236 ‐
Marketplace $ 5.17 $ 6.23 145 ‐ 145 ‐
Total $ 1.43 $ (0.48) 4,891 4,018 873 22%

Customer Value by Acquiring Media
• Understand the real value of media by looking at the downstream value of
buyers – that is the rest of the equation
1st Time Buyer 12 Month Activity Post Initial Purchase
Subsequent
12M 1st Time 1st Order 1st Order Subsequent 12 Mo Orders/New Demand/Ne
1st Order Promotional Media Custs Demand AOV 12 Mo Orders Demand Customer w Customer
Catalog 22,111 $1,587,013 $ 72 $5,820 $468,445 0.26 $21.19
Natural Search ‐ Branded 5,906 $443,547 $ 75 $1,195 $118,929 0.20 $20.14
Natural Search ‐ Non Branded 4,961 $287,175 $ 58 $786 $65,575 0.16 $13.22
Paid Search ‐ Branded 966 $78,075 $ 81 $95 $10,680 0.10 $11.06
Paid Search ‐ Non Branded 935 $57,866 $ 62 $54 $2,890 0.06 $3.09
Advertising 18,637 $1,319,660 $ 71 $4,063 $411,634 0.22 $22.09
Email 7,054 $782,065 $ 111 $1,554 $231,113 0.22 $32.76
Grand Total 60,570 $4,555,400 $ 75 $13,567 $1,309,265 0.22 $21.62

12‐Mo Demand per Buyer by Media a er Acquisi on
$32.76
25,000 22,111 $35.00
$ Deamdn a er Acquisi on

18,637 $30.00
20,000 $22.09
$21.19 $25.00
# of New Buyers

$20.14
15,000 $20.00
$13.22
10,000 $11.06 $15.00
5,906 7,054
4,961 $10.00
5,000 $3.09
966 935 $5.00
‐ $0.00
Catalog Natural Natural Paid Search Paid Search Adver sing Email
Search ‐ Search ‐ Non ‐ Branded ‐ Non
Branded Branded Branded
12M 1st Time Custs Demand/New Customer

12

9/18/2012

Downstream Media Response
• Identify variances in how customer acquired by different media respond to
other media downstream

Campaign Performance
• Compare campaigns across different media using the same metrics

13

9/18/2012

Product Performance by Media
• Compare merchandise sales by different media Appliances

Demand by Media Catalog SEO‐Brand SEO‐Other SEM‐Brand SEM‐Other Email Other Online Total Other Online
Appliances $283,477 $23,247 $17,886 $3,028 $31,093 $28,883 $132,816 $520,430 26%
Food/Cooking $609,122 $67,180 $37,970 $37,705 $481,807 $147,761 $107,151 $1,488,696
Email Catalog
Garden $167,907 $16,453 $5,999 $6,823 $9,928 $32,600 $22,997 $262,707
Books $75,566 $7,158 $2,877 $2,951 $1,159 $15,994 $12,774 $118,479 SEM‐Other 6% 54%
Other $76,382 $9,357 $1,807 $2,653 $463 $9,446 $24,909 $125,017 6% SEM‐Brand
Personal Care/Clothing $88,309 $5,332 $2,866 $2,756 $349 $13,677 $9,047 $122,335
Books/DIY $12,610 $1,388 $267 $421 $1,005 $6,167 $7,109 $28,967 1%
Gifts $42,168 $2,222 $1,912 $1,399 $2,075 $6,726 $4,588 $61,091 SEO‐Other SEO‐Brand
Housewares $240,118 $27,985 $20,271 $12,776 $8,244 $56,617 $39,067 $405,077 3% 4%
Tools $173,669 $21,974 $15,946 $9,247 $14,575 $50,377 $33,193 $318,981
Lighting $167,641 $24,718 $17,882 $14,796 $3,585 $55,212 $37,360 $321,194
Toys $22,422 $2,301 $1,787 $1,059 $564 $6,596 $2,717 $37,445
Total $1,959,391 $209,315 $127,468 $95,614 $554,847 $430,057 $433,727 $3,810,419

Percent of Total for Media Catalog SEO‐Brand SEO‐Other SEM‐Brand SEM‐Other Email Other Online Total
Appliances 14.5% 11.1% 14.0% 3.2% 5.6% 6.7% 30.6% 13.7%
Food/Cooking 31.1% 32.1% 29.8% 39.4% 86.8% 34.4% 24.7% 39.1%
Garden 8.6% 7.9% 4.7% 7.1% 1.8% 7.6% 5.3% 6.9%
Books 3.9% 3.4% 2.3% 3.1% 0.2% 3.7% 2.9% 3.1%
Other 3.9% 4.5% 1.4% 2.8% 0.1% 2.2% 5.7% 3.3%
Personal Care/Clothing 4.5% 2.5% 2.2% 2.9% 0.1% 3.2% 2.1% 3.2%
Books/DIY 0.6% 0.7% 0.2% 0.4% 0.2% 1.4% 1.6% 0.8%
Gifts 2.2% 1.1% 1.5% 1.5% 0.4% 1.6% 1.1% 1.6%
Housewares 12.3% 13.4% 15.9% 13.4% 1.5% 13.2% 9.0% 10.6%
Tools 8.9% 10.5% 12.5% 9.7% 2.6% 11.7% 7.7% 8.4%
Lighting 8.6% 11.8% 14.0% 15.5% 0.6% 12.8% 8.6% 8.4%
Toys 1.1% 1.1% 1.4% 1.1% 0.1% 1.5% 0.6% 1.0%
Total 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

Product Purchase Propensity
• Marketing Databases with order detail contain a wealth of predictive
value
– Note that product categorization useful for marketing is often not
the same as categorization used by merchants

14

9/18/2012

Order Value Analysis
• Replace “Average Order” with Order Value profiles for added insight
• Averages don’t tell the story
• Compare different media

Putting It Together

15

9/18/2012

Top Down Reporting
• Create a high level marketing dashboard relevant to your business
• Detail can be provided in a series of supporting reports, as needed

Campaign Support
• Classic Predictive Response – Recency, Frequency, Monetary
– Simple Bracketing
– Model Scoring
• Order Method – Store, Website, Call Center
– Less predictive for direct channels than in the past
• Media Responsiveness
– Predictive, but hard to measure
– Test to validate measurement criteria
• Product Preferences
– Categorization should be customer‐driven
– Often not the same categories as merchants use

16

9/18/2012

Reporting and Tools
• Business Intelligence – Classic Approach
– Business Objects
– Cognos
– SAS
• Analysis Tools – Interactive Approach
– Tableau

Tips
• Remember, tailor your reporting and target metrics to your business
• Behavioral science requires both qualitative and quantitative analysis
• Don’t be afraid to apply an 80:20 solution – remember opportunity cost
• Start at the top and then drill down
– When stuck go up a level
• When presenting to management, start minimalist
• Transactional costs not reported through your marketing database can be
estimated from a P&L and added as a cost per order

17

9/18/2012

The Future
• Understanding the full consumer picture – moving from unknown to known behaviors

Acquire Engage Convert Maximize
Target site
Searched for content pants,
shoes handbag

Purchased
Browsed
shoes Purchased
shoes, pants
handbag
Clicked on a
handbag
display ad Loyalty club
invite – high-
value offer
Email cross-
sell pants,
handbag
High-value
shoe content /
offer

• Display click: pants • Interest in shoes,
• Search on shoes pants, handbags
• Site visit: shoes, pants • Purchased shoes
• Displaying high-value • Email & location
browsing behavior • High value

Al Bessin
Vice President, Specialty Retail
Merkle
abessin@merkleinc.com
512.745.9070

Questions and Answers

Competitive advantage in the future will live
in how effectively an organization can
understand, track, engage, measure &
influence consumer behavior.

18

Douglas Newell
Doug Newell is founder and Managing Director of Calexus Solutions LLC. He is a
successful serial entrepreneur with over 30 years’ experience in leading major analytic and
systems integration efforts.

In 1998 Doug founded Genalytics, Inc. Genalytics (now Semcasting Inc.) is a developer of
automated analytic software. Its core genetic algorithm based data mining software has been
licensed by the majority of major US financial services organizations.

Prior to founding Genalytics, Doug was a Founder and Vice President of Quantitative
Solutions and Management Consulting for Tessera Enterprise Systems. He was also the
Founder and General Manager of the firm’s successful European subsidiary, Tessera GmbH,
headquartered in Munich. Tessera and Tessera GmbH provided systems integration services
to large retailers, brokerages, insurance companies and banks such as UBS Bank in Zurich.

Earlier in his career, Doug served as Vice President of Analytics and Consulting for the
High-Performance Computing Division of Epsilon Data Management/American Express.
While at Epsilon, Doug established the Epsilon Analytic Consulting Group. This
organization grew to become one of the direct marketing industry’s largest and most capable
analytic teams.

Doug has been recognized as an innovator in the world of analytics and a pioneer in the
application of machine learning technologies. He and his teams have supported American
and European firms across a myriad of industries. They have provided novel solutions to
challenges in marketing, site selection, risk management, fraud detection, and healthcare.

Doug Newell earned a degree in economics from Washington and Lee University, and an
MBA from the College of William and Mary.

9/18/2012

Embedded
Intelligence,
the Next Generation
of Analytics
Presented by;

Doug Newell
Calexus Solutions

We all look for
patterns all the
time. (This one is from
a test of pattern
recognition software.)

1

9/18/2012

There are
predictable
patterns in
innovation that
are shaping our
world.
Matthew Brady:
Expert
Photographer

Early Brownie
Camera: a camera
Disseminated to the
average man.

2

9/18/2012

Cell Phone with
Embedded
Camera

And they are predictable
because they repeat

Expert running
early computer

3

9/18/2012

A computer
Disseminated to
the people

And they are predictable
because they repeat

Tablet with Embedded Computer

4

9/18/2012

And so it is with marketing analytics
Phase 1: Experts
(1888 Regression Invented)

Phase 2: Mass Dissemination
(1980’s Various Stats Software Packages)

5

9/18/2012

Phase 3: Embedded Marketing Analytics
(2011-now)
• Similar to automated stock market
trading
• Millions of marketing decisions are made
without human intervention in real time
• Rules and reports provide safeguards
• Complex algorithms optimize savings on
each of millions of decisions yielding
major improvements in cost
effectiveness

Applying Embedded Analytics
in Marketing

6

9/18/2012

Basic Sales and Marketing Concept
Sources of
Prospects Sales Process Sales

$
Profits

Big Idea
• At each critical point in that flow there are
opportunities to embed analytics in the form of
scoring equations.
• These equations can perform many tasks such as:
– Qualifying and Prioritizing leads
– Identifying Bottlenecks
– Bid Optimization (e.g. Paid Search)
– Identifying critical marketing factors
– Measuring efficacy of marketing actions
& investments

7

9/18/2012

Case Study #1
Insurance Innovator

Goal: Become profitable
• Strategies:
• Keep the most profitable
prospects
• Sell off the poor prospects
• Streamline the process

• Methodology: Embed analytics at critical
decision points and direct prospects down
the appropriate path based on their profit
potential

8

9/18/2012

Original Process
Original cumbersome
Affiliates
process treats all
prospects equally

12 Pages of Questions
Aggregators

Returned Quote
Request Quote
Landing Page

3 Pages of
Questions

Quote
Final
1% Sold

Net loss
Organic of
$100MM
over 10
Years

Paid Search

Analytics Applied to Early
Decision Points

1

2

3

4

9

9/18/2012

Embedding Analytics at
Critical Decision Points
Start predicting customer value as soon as you encounter
the customer
1 Aggregators Predict customer value
Predict customer value based on the affiliate
based on the
source and initial
aggregator source
Affiliates 2 information provided
and initial information
provided

3 Paid Search Predict customer value
Key word Optimization
based on using on
based on expected
certain keywords
long term value
Organic and ads at specific
4
times

5
Refine your prediction of customer value as
you learn more about the customer

Most valuable prospects
By combining minimal input data from the
Good prospect with appended data from Acxiom,
Landing Page

prospects Model 5 predicts an adjusted customer value:
• The most valuable prospects are sent to
5 the call center who have a much higher
Least
sales close rate, but also cost much more to
profitable use
prospects • Those prospects who previously would have
likely resulted in a net loss are now sold
to a 3rd party for a profit

10

9/18/2012

6
Streamline the process based on information already
gathered
In the original process, a majority of
customers lost patience during the
12 page interview process.

Interview 6 By predicting answers based
Process
on previously asked questions,
Model 6 cuts the interview process
in half, greatly reducing the
abandonment rate.

7
Streamline the offers based on
information already gathered
In the original process, the initial quote
Additional Questions

estimate was often much higher or
Returned Quote

Final Quote

much lower than the actual price.
Both situations caused prospects to
abandon the process.
Using information from the first set of
7 questions, a model was created that
returned an initial quote estimate
much closer to the final quote.

11

9/18/2012

Additional Strategic Analytic Embedded Models:
Retention Duration Estimate – Sell once, get
many renewals
(Do this first!...It feeds into Long Term
Profitability)
Long term Profitability – Drove many of the other
models; finds not just new customers but
profitable new customers (Do this second… it
drive most other decisions) (This may need
revision as you learn more about the data and
the business dynamics.)

Results of Embedded Analytics
Through embedded
analytics, in a one year
period, this company
succeeded in:
• Keeping their most
profitable prospects
• Selling off their poor
prospects
• Streamlining their
process

After years of consistent losses, they have achieved
their goal of profitability.

12

9/18/2012

Case Study #2
Real-time Internet Ad
Bidding Optimization

Background

• Internet Display Ads are about $12
billion/year market
• Mirror direct marketing in many ways
– Compiling of lists
– Use of Segmentation prior to advent of more
sophisticated modeling
– But much bigger volumes…
20 billion per day

13

9/18/2012

The Innovations that Enable
Real-time Bid Optimization
Machine Real Time
Learning Bidding
Technology
Based on genetic An exceptional
algorithms: test tens bidding engine
of thousands of sifting through over
equations to get the Success! 578k views per
best fit. The second and
technology is made accessing the most
for big data, and promising users;
enables rapid model directly interacts
development. with AppNexus.

The Genetic Algorithm
• Genetic Algorithms use the
concept of Natural Selection
• Models continuously iterate
(breed), keeping the
strongest solutions found
(learning).
• Over millions of
champion/challenger tests,
a superior solution evolves.

14

9/18/2012

The Real Time Bidder
The most popular internet
Replies with Bid
bidder sends a bid request (50 milliseconds)
stream to a trading desk
about 20 billion times per
day, whose bidding engine
Bid Request Stream
replies within 50 User ID (Cookie)
milliseconds. Time of Day
Day of Week
The BRS contains Trading Browser
Operating System AppNexus
information that we work Desk Publisher
with, using the genetic Age
Gender
algorithm to create an Region
informed solution that gives City
Data Segments
clients’ bidders smarter
equations. When integrated
into the bidder, these
equations allow clients to Sends BRS
(20 billion times per day)
purchase more promising
bids at a better cost.

The Real Time Bidder
Replies with Bid
(50 milliseconds)

Bid Request Stream
User ID (Cookie)
Time of Day
Day of Week
Browser
Informed Real Time Operating System AppNexus
Equations Bidder Publisher
Age
Gender
Region
City
Data Segments

Sends BRS
(20 billion times per day)

15

9/18/2012

When you know the value of an ad
impression better than anyone else…
Genetic
Algorithm Based
Bids
Opportunity
Actual Value

Competitive
Bids

Waste

% impressions bid

It’s like card counting….

A Combination for Success:

A Food Chain
Mining for new customers using
Real-time (embedded)
Bid Optimization

16

9/18/2012

Beginning:
They had been doing data mining the old
fashioned way…Getting Their Hands Dirty
• The firm started out winning bids,
but were not getting a great
return. Their Cost-Per-Conversion
was $57.
• They put out 591K bids per day,
and were spending $28K/month.
• Certainly, this strategy earned
some conversions, but their
strategy could use some help.

Middle:
A Little Gold (new customers), A Lot of
Junk (wasted ads, wasted money),
and A Big Change

Calexus realized the food chain was winning
a lot of bids, but they weren’t all good bets.

17

9/18/2012

Middle:
A Little Gold, A Lot of Junk, and
A Big Change

By implementing the cutting edge bid optimization
technology, the food chain get only the bids they
actually wanted.

End:
Innovation Leads to Celebration
The company saved an average of 44%,
saving some 90% in certain locations.
The combination of
technological breakthroughs
(machine learning and real time
bidding) joined together to
surpass the client’s acquisition
goals while significantly cutting
their costs.

18

9/18/2012

Ingredients for
Success

Ingredients for Success
Executive buy-in
• Need for change
• Executives (CEO, CMO, CTO, CIO) get
it!
• Excited about potential improvements
and resulting profit
• Supports the process with needed
resources

19

9/18/2012

Machine Learning has arrived
• Genetic Algorithms, Neural Networks, etc.
• Once exotic, now taught in many college
curriculums
• Show the algorithm a set of data many
times; it learns the predictive pattern
• Apply that pattern to predict future events
to support business decisions

A Digression-
Machine Learning Advantages
Fast
• Creates 26 models per week instead of 26 models
per year
• Allows for quick model re-creation and adjustment
Accurate
• Searches masses of data for subtle predictors
• Critical for Internet related analyses where the data
is sometimes measured in terabytes
• More data = more accuracy
Transparent
• No Black Box, marketers must understand what
is driving recommended decisions

20

9/18/2012

Strong Technical Support Team
• Expertise involved in embedding and validating the
models
• Opportunity for PMML?

Data Capture and Organization
(rating of at least “fair”)
• Don’t wait for perfection; it’s not coming
• A modern robust modeling technology should
accommodate some dirty/missing data


Vision
“Some people see things as they
are and say why. I dream things
that never were and say why not?”
G.B. Shaw

21

9/17/2012

Navigating the Data Maze
Randy Watson
Vice President
Acxiom

What We’re Covering
• Consumer-Oriented Data
• Marketing Data
• Data in the U.S.
– How / What’s Available Globally

2

1

9/17/2012

The Big Data Deluge
VOLUME
> There were 5 exabytes of data created between the dawn of civilization
through 2003…that much information is now created every 2 days
VARIABILITY

> 80% Of data growth will be in the form of semi-structured and
unstructured data

> IDC predicts that between 2009 and 2020 digital data will grow 44x to
VELOCITY

35 zettabytes

VALIDITY

3

Categories of Data
Demographic /
Descriptive

Promotion History
All Channels

Behavioral
View, Response

Interest / Lead

Transaction
Buy, Act

Credit / Risk

Research / Survey

Business to
Business

Social / Relationship
4

2

9/17/2012

Levels of Data

Individual

Household

Geographic
ZIP®*, DMA, Cable Zone

Cookie Groupings Device
Known, Anonymous Behavioral, Lifestage, IP, Mobile Device /
Social / Family Machine

*The following trademarks are owned by the
United States Postal Service®: ZIP®

The Power of Focus
Demographic /
Descriptive

Promotion History

Behavioral

Interest / Lead

Transaction

Individual Household Geographic Groupings

Credit / Risk

Research / Survey

Business to
Business

Social / Relationship
6

3

9/17/2012

Marketing Data Value

Customer
Data

Leads
Event / In Market

Behavioral
Purchases / Categories / Interests

Descriptive
Demographic / Interests / Household

7

Balanced Data
• Number of records • Amount of data per record
• Percent of population • Number of elements
covered • “Match rate”
• “Coverage” or “reach”

Breadth Depth

Accuracy

• How “true” is it
• How “precise” is it

8

4

9/17/2012

Channels / Uses for Data
Channels Uses
Mail Analytic Models
Telephone Target / Selection
Email What to Offer
Online / Web Traditional Timing of Offer
Mobile (test or display) Messaging / Scripting
Radio Creative
Billboards Channel Decisioning
Magazines Customer Service
Television Content Optimization

9

1
2
3
4
5
Categories in Action
6
7
8
9

Demographic - Descriptive Promotion History Behavioral
• Age / Gender / Education / Occupation • Mail, Email Offers • Interests from Any Channel – Finance,
• Net Worth / Income / Children • Online Ads Sports, Health, Causes
• Segmentation Lifestage • App Ads, Text • Search
• Geography • TV • View – Websites, TV Programs, Ads
• Property / Auto • Print • Respond – Open Email, Click Email,
Click Ad, Enter URL
1 2 3

Interest / Lead Transaction – Buy, Act Credit / Risk Data
• Behavior Based • Categories: Apparel, Jewelry, • Personal Credit / Score
• Hand Raiser – Declared Electronics, Automotive, etc. • Business Credit
• Event Based – Divorce, Marriage, Birth, • Payment Types • Bankruptcy / Foreclosure
Graduation, Home buyer, Car Buyer • Channel of Action – Brick & Mortar, Mail, • Collection
Phone, Online
4 5 6

Research / Survey Business to Business Social / Relationship
• Attitudes • SIC / NAICS Codes • Potential Inheritors
• Media Habits • Employee Size • Adults w/ Elderly Parents
• Brand Loyalties • Sales Volume • Adults w/ Wealthy Parents
• Technology Adoption • Contacts / Titles • Social Networks
• Social Groups & Relationships
7 8 9

5

9/17/2012

About Acxiom
• Acxiom is a recognized leader in marketing services and technology
that enable marketers to successfully manage audiences,
personalize consumer experiences and create profitable customer
relationships
• Our superior industry-focused, consultative approach combines
consumer data and analytics, databases, data integration and
consulting solutions for personalized, multichannel marketing
strategies
• Acxiom leverages over 40 years of experience in data management
to deliver high-performance, highly secure, reliable information
management services
• Founded in 1969, Acxiom is headquartered in Little Rock, Arkansas,
USA, and serves clients around the world from locations in the
United States, Europe, Asia-Pacific and South America

11

Thank You!

If you didn’t get a chance to stop by our booth
Visit us at
www.acxiom.com/DMA12
and find out how we’ve helped brands
“Navigate the Data Maze”
to get
Better Connections. Better Results.

6

3

From Information to Audiences:
The Emerging Marketing Data Use Cases
A Winterberry Group White Paper
January 2012

© 2012 Winterberry Group LLC.

Acknowledgements
This white paper would not be possible without the significant contributions of more
than 175 advertising and marketing thought leaders—representing virtually all corners
of the commercial data and technology ecosystem. In particular, Winterberry Group is
grateful to our research partner, the Interactive Advertising Bureau, as well as the
following sponsors for their generous support of this important research initiative:

Presenting Sponsors:

Supporting Sponsors:

To all those whose insights, time and other contributions helped in the development
of this white paper, we thank you.

Notice
This report contains brief, selected information pertaining to the commercial
marketing data industry and has been prepared by Winterberry Group LLC with the
support of Interactive Advertising Bureau. It does not purport to be all-inclusive or to
contain all of the information that a prospective investor or lender may require.
Projections and opinions in this report have been prepared based on information
provided by third parties. Neither Winterberry Group, the Interactive Advertising
Bureau nor their respective sponsors make any representations or assurances that
this information is complete or completely accurate, as it relies on self-reported data
from industry leaders—including advertisers, marketing service providers, technology
developers and agencies. Neither Winterberry Group, the Interactive Advertising
Bureau nor any of their officers, employees, representatives or controlling persons
make any representation as to the accuracy or completeness of this report or any of
its contents, nor shall any of the foregoing have any liability resulting from the use of
the information contained herein or otherwise supplied.

2


Executive Summary
No matter what analogy you prefer, one truth is undeniably clear: Technology has
fundamentally advanced the creation of what many call “big data.” Consider:

 From the dawn of time through 2003, according to Google’s executive
chairman, Eric Schmidt, human civilization generated approximately 5 exabytes
of aggregate information. In 2009, that much data—captured in the equivalent
of 25 quadrillion tweets—was generated every two days
 Globally, businesses created 1.8 zettabytes of data in 2011, according to IDC.
That output—enough to fill 57.5 billion 32-gigabyte Apple iPads—is growing
approximately 62 percent annually (on a compounded basis)
 In July 2011, Facebook’s 750 million worldwide users uploaded approximately
100 terabytes of data every day to the social media platform. Extrapolated
against a full year, that’s enough data to manage the U.S. Library of Congress’
entire print collection—3,600 times over.

The world’s Twitter feeds, iPads and libraries may not stand a chance against this
onslaught of information. But to the world’s marketers, the proliferation of data has
given rise to what may prove to be the most substantial commercial opportunity since
the emergence of the World Wide Web: the ability to better understand consumers,
seamlessly match “right-time” offers to their needs and optimize the management of
profitable, long-term customer relationships.

The ongoing Not surprisingly, many are working feverishly to capitalize on the new potential of
marketing data, especially with respect to the torrent of highly insightful (but highly
convergence of unstructured) information being generated online. The ongoing convergence of new
new data data sources, targeting technologies and advertising delivery platforms is likewise
sources, shifting their focus—from the management of raw information to the optimization of
granular consumer audiences across discrete advertising channels, product categories
targeting
and geographies.
technologies and
advertising The demands of real-time, rules-driven, audience-centered marketing represent a full-
delivery on paradigm shift in how marketing is done. But with the opportunity inherent in this
approach comes a daunting challenge: to identify and deploy an actionable range of
platforms is “use cases”—practical marketing applications that, fueled by data, may drive
shifting focus— transformative improvements in both marketing effectiveness and efficiency.
from the
management of Today, even while some enjoy modest success in redeploying their existing resources
to the new cross-channel task, most other marketers—saddled with legacy technology
raw information platforms, depleted of expertise by years of underinvestment and structured only to
to the support “traditional” approaches to data usage—are finding they’re woefully
optimization of unprepared for this transformation. For them, a growing data divide is taking shape,
distinguishing those use cases to which data may now be profitably deployed from
granular
those which—though promising in their strategic potential—still represent nothing
consumer more than ideals of how automated, multichannel marketing may someday take
audiences. shape.

3


This white paper—produced in conjunction with the Interactive Advertising Bureau—
will explore four data-driven use cases (audience optimization, channel optimization,
advertising yield management and targeted media buying) that collectively represent
the foundation of how many are now seeking to leverage the potential of “big”
marketing data. In addition to that analysis, it will demonstrate that capitalizing on
this opportunity will require:

 Rules-driven integration of disparate data sets: The collection, analysis and
segmentation of digital data demands the aggregation and anonymization of
virtually all data, challenging marketers’ fundamental ability to draw distinct
insights from consumers’ cross-channel interactions
 Improved operating infrastructures: Though substantial process and data
structure challenges also exist, a substantial barrier now inhibiting wider
marketing data optimization resides within the marketing organization—
characterized by rigid “silos” and the paucity of data-savvy marketing
operations, IT and sales talent
 A strong network of data-centric technology and service partners: The fastest
and most efficient data aggregation, analysis and throughput solutions require
a strong ecosystem of partners who understand and can integrate seamlessly
with core data assets and supporting technologies
 Marketing data governance: While organizations have long employed policy
experts to advise on the regulatory ramifications of data utilization, many are
coming to see marketing data governance—defining the “rules of the road” for
assigning distinct data sources to different promotional tasks—as equally
important.

4


Methodology
This white paper explores a series of “use cases” that define how marketers are
commonly deploying multichannel data to improve their advertising and marketing
effectiveness and efficiency. It further highlights a series of trends that are defining
how data is now being used to drive broader advertising and marketing performance
for companies based in the United States.

Developed in research partnership with the Interactive Advertising Bureau—and with
the sponsorship of IBM, BlueKai, eXelate, Janrain, ShareThis and V12 Group—the
paper’s findings are based on the results of an intensive research effort that included
in-person, phone and online surveys of more than 175 marketers, agency executives,
data compilers, technology developers and other industry thought leaders around the
globe.

Where possible, contributors have been cited by name so as to provide transparency
into the research process and supporting panel. In some cases, contributors have
asked that we omit their name and company information so as to allow them the
freedom to speak with maximum candor.

5


The Emerging Marketing Data Use Cases
The span of today’s data use cases is broad, reflecting the relative immaturity of the
“digital data” enterprise and the array of pilot solutions that marketers and publishers
are deploying to make use of the growing information resources at their disposal. For
some, a data use case may be as simple as demographic-driven customer acquisition
(as enabled by a rented mailing list); for others, the span of what’s actionable may
include a host of sophisticated display advertising targeting solutions.

Interest in these applications is being piqued by the realization that information may be
used to drive transformative value that spans “demand” and “supply” sides of the
advertising and marketing value chain. Data availability is now allowing advertisers,
agencies and publishers to optimize ad delivery, evaluate campaign results, improve
site selection and retarget ads to other sites. It’s also improving the value of media to
brands by delivering their advertising to better-qualified prospects—making the ad
more efficient, more valuable and providing a more compelling user experience.

Grounded in years of direct response, data use by those marketers that predominantly
leverage offline channels is proving to be just as sophisticated as those applications
that dominate in the online sphere. Ironically, best practices developed in this
“traditional” DR marketing world are often used to establish parameters for the
deployment of digital data, even in those cases where data are being used to enable a
shift in strategic emphasis from direct response to brand engagement. “The industry
has spent a lot of time and money at the bottom of the funnel,” said Jeff Liebl, chief
marketing officer at TruSignal. “Advertising is supposed to be about generating intent,
but the bottom of the funnel is mostly about looking for people who have already
shown interest. I think we’ll see ad dollars shift and a greater focus placed on earlier,
upper-funnel brand awareness activity, targeting people that haven’t necessarily
demonstrated online behavior yet that shouts ‘I’m in market.’”

What follows is a discussion of four selected marketing data use cases—audience
optimization, channel optimization, targeted media buying and advertising yield
management—along with an assessment of fundamental benefits, current maturity
levels, core beneficiaries and long-term potential.

Use Case Fundamental Maturity Core Beneficiaries Long-Term
Advertising Benefit Level Potential
Audience Optimization Effectiveness Low E-commerce Marketers, Digital Advertisers, High
Lead Generation Portals, Publishers (for
traffic acquisition)
Channel Optimization Effectiveness/Efficiency Low E-commerce Marketers, Publishers, Lead High
Generation Portals
Advertising Yield Efficiency Low Publishers High
Optimization
Targeted Media Buying Efficiency/Effectiveness Intermediate Marketers (via Demand-Side Platforms), High
Digital Agencies / Trading Desks

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7


Audience Optimization
Identifying customers and likely prospects through the integration of rich (though
disparate) first- and third-party data sources; managing cross-channel marketing
execution with the goal of engaging those audiences strategically—and in
accordance with consumers’ preferred advertising media.

Fundamental Maturity Core Long-Term
Advertising Benefit Level Beneficiaries Potential
Effectiveness: Identifying Low: Though the technology now E-commerce High: More so than any other
the “right” target exists to capture and deploy large Marketers, Digital use case, the ability to define
consumers is the foundation quantities of information (in the Advertisers, Lead high-potential audiences from
of targeted advertising, and necessary “real-time” windows), Generation Portals, disparate indicators—and then
may be used to improve consensus has yet to coalesce Publishers (for communicate with them across
performance across around the optimal approach to traffic acquisition) a range of media—represents a
branding, engagement and structured integration of this fundamentally new approach
direct response functions data—especially when its sources to managing addressable
span traditional (“PII”) and digital customer markets
(“non-PII”) channels

The plethora of first-party data now being amassed and analyzed by both publishers
and advertisers is being used to build rich audience profiles that, marketers say, can
enhance advertising effectiveness by enabling improved targeting and message
relevancy. Today’s dominant approach calls for the development of unique
customer/prospect profiles, which are then segmented and modeled as the basis for
identifying what are commonly called “lookalike audiences” for follow-up marketing
across channels.

For publishers, third-party data overlays and data exchanges—providing access to a
wealth of additional information generated through online sources—are providing the
opportunity to enhance first-party data with demographic and interest-based
indicators, as well as first-party data from other online publishers. “Companies usually
own very rich first-party data,” said Travis May, head of strategy and operations at
Rapleaf. “Third-party data is especially helpful when there are new customers or early-
lifecycle customers and the data need to be enhanced to be segmented more quickly.”

In one example: Catalina Marketing, which claims to collect and analyze in-store
purchase data covering 80 percent of the U.S. population, is now combining offline and
online sales data to help its consumer goods clients make more intelligent, audience-
centric predictions for in-store promotions. According to Eric Williams, Catalina’s chief
information officer, this approach is generating 8-10 percent coupon redemption rates
(versus 0.5 percent rates for comparable mass-market couponing programs).

“By linking this data, we are creating a total purchase history that will allow us to
categorize and stratify consumers into purchase category buckets and infer what will
be of interest to them before they actually buy,” said Williams.

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Channel Optimization
Enabling “right message, at the right time, via the right media” targeting; expanding
the role of consumers in choosing optimal/preferred communications media.

Fundamental Maturity Core Beneficiaries Long-Term
Effectiveness/Efficiency: Low: Traditional advertising and E-commerce High: Migration to media-
Allows for the strategic marketing efforts have been Marketers, Publishers, agnostic communication
utilization of media in structured around the Lead Generation strategies stand to enhance
alignment with the inherent deployment of individual channels Portals consumer engagement,
strength of those channels, through distinct campaigns, and promote a robust dialogue
as well as consumer migration to true “media- and reinforce both single-
preferences; engages agnostic” models that seek to purchase behavior as well as
audiences at a richer level match audiences to lifetime customer value
and minimizes investment in optimal/preferred output levers
wasted/suboptimal channel requires process, technology and
efforts data source alignment that most
marketers have not yet
undertaken

The rapid introduction of new addressable marketing channels over the past two
decades—starting with the emergence of foundational digital media such as email,
search and display advertising, and hallmarked today by the maturation of tablets,
smartphones, addressable television and other media—has reinforced consumers’
technological sophistication, and provided them with a new span of control over
marketing content. At the same time, the diversity of promotional options has
introduced a new challenge to both publishers and advertisers: maintain a marketing
dialogue that matches strategic intent to optimal delivery channel, but honors
consumers’ choice with respect to messaging cadence and medium.

Brands that are able to integrate multichannel data across channels—effectively
becoming “agnostic” to the deployment of any single medium—hold the prospect of
creating holistic, near-360-degree views of customer preferences and intent regardless
of channel. The result is more relevant advertising—delivered at the optimal time, via
the consumer’s preferred channels.

Executives across the marketing ecosystem agree that data owners are sitting on
mountains of valuable information that can be used to drive these kinds of media-
agnostic efforts, but say much of the potential of that data is being undermined by
efforts to deploy messages through “sexy” channels, such as social media platforms.
“Marketers are anxious to jump ahead into social and other burgeoning areas of digital
marketing, yet they shouldn’t overlook that they have a tremendous asset right on
their own website that can be used to make these efforts more effective,” said Marc
Kiven, founder of BrightTag. “Imagine being able to walk behind every customer in your
store and see where they go, what they look at and what they touch. This data already
exist… *marketers+ just need permission to use it and the technology to unlock it.”

9


Advertising Yield Optimization
Maximizing the value of available advertising inventory by identifying and “selling”
high-value audiences across individual publisher properties and delivery media.

Fundamental Maturity Core Long-Term
Advertising Benefit Level Beneficiaries Potential
Efficiency: Allows advertisers to Low: Though technological Publishers High: For a publisher community
avoid investing in media on the advances are rapidly struggling to effectively monetize
basis of simple demographic allowing audiences to be content (both “premium” and
characteristics—where “sold” across distinct online among “long tail” sites that
impressions generally reach a media platforms, the generate less Web traffic), the
large number of suboptimal potential of the approach identification and optimization of
target consumers as a means of demands true cross-channel audience-centric inventory has
capturing good prospects from a yield optimization; most the potential to deliver
larger universe. (To publishers, publishers are very early in substantial revenue
the benefit is all about their efforts to integrate opportunities, possibly even
effectiveness—as optimizing traditional ad inventory supplanting existing approached
yield generates higher (where it exists) into a to advertising packaging and sales
advertising revenues) holistic optimization effort

On the supply side, publishers are moving fast to deploy third-party data overlays
(sourced largely through exchanges) and the services of data management platforms in
an effort to create richer audience profiles designed to maximize their yield (the rates
they may charge for advertising inventory) and improve the value of that ad inventory
for which traffic doesn’t warrant a “premium” sales approach or pricing.

With multiple data streams, typically, feeding internal systems in rapid succession,
publishers said data control, accuracy and processing speed are critical prerequisites
for identifying high-yield audiences across disparate media platforms. “We have two
big relationships with publishers and both recognize the need to control their data
ecosystem in a very robust way,” said David Soloff, chief executive officer of
Metamarkets. “They are carefully overseeing first- and third-party data and usage logs
and trying to uncover tremendous pockets of inventory that may be mispriced or
ignored. It’s great for building ROI.”

One publisher said that the benefits of yield optimization ultimately won’t stop with
more informed pricing of inventory. “Creative versioning,” he said, will allow
advertisers to provide variable, tailored content to different audiences across all of the
publisher’s properties—enhancing the effectiveness of each ad unit (while driving the
publishers’ ability to extract value from that inventory). “We can execute this idea now
on any given property, but we’re working on a way to be able to roll this out across all
of our sites,” the publisher said.

One major challenge, he added, has already surfaced as a barrier to capitalizing on this
potential: the ability and willingness of advertising sales teams to understand, embrace
and communicate the role of these complex ad units.

10


Targeted Media Buying
Enabling the economical, value-oriented purchase of advertising media; delivering
targeted messages to audiences across a diverse, actionable range of channels.

Fundamental Maturity Core Beneficiaries Long-Term
Efficiency/ Intermediate: “Real-time Marketers (via High: Meaningful media-
Effectiveness: The use of bidding” (RTB) tools have Demand-Side buying efficiencies are
automated, real-time media matured substantially over Platforms), Digital already accruing to
buying tools allows for access to the past few years, and are in Agencies / Trading sophisticated users; deeper
audiences at “true” market common use by enterprise Desks value will come through the
pricing—eliminating the need to marketers across verticals coordinated use of these
invest in “eyeballs” that are not applications and the targeted
likely to value a message or messaging/offer tools that
offer; likewise provides a deeper allow for optimization of
platform for customizing message content, timing and
marketing offers or content in a cross-channel integration
move to expand relevance of
those underlying messages

Demand from advertisers for the efficiencies inherent in real-time bidding and the
improved effectiveness that comes through improving brand messaging relevance is
driving more sophisticated data use across both ad targeting and media buying
practices. Demand-side platforms (DSPs) and digital agencies (many empowered, over
the last few years, by the addition of automated trading desk capabilities) are leading
the market in this respect by enabling marketers to identify, “purchase” and target
high-value customers across channels, in rapid timeframes.

In particular, search and display retargeting programs—targeting site visitors who have
abandoned a shopping cart or left a site without otherwise converting—can provide
specific offers based on the visitors’ on-site behavior. “By way of example, one of our
retail clients… wanted to establish dynamic targeting rules as its customers came onto
its site,” said BrightTag’s Kiven. “By splitting its audience into control and test groups,
the retailer was able to understand the differences in behavior of users who saw a
retargeted message versus those who did not.” The results of this more flexible, rules-
driven approach to data collection and integration lets the company shift attention
from top-of-funnel branding efforts and work more closely with its DSP partner to
better manage retargeting bids.

Multichannel data integration is a critical component of improved media-buying
capabilities. According to one agency executive, integrating on- and offline data for one
of the agency’s advertising clients resulted in a nearly 30 percent increase in online
display performance.

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The Opportunity Ahead: Trends in Marketing Data Utilization

Rules-driven integration of disparate data sets: While traditional data management
practices were built largely around centerpiece “personally identifiable information”
(PII) elements—usually consumer names and postal addresses—the collection,
analysis and segmentation of online data demands the aggregation and
anonymization of virtually all data sets, challenging marketers’ fundamental ability to
draw distinct insights from consumers’ cross-channel interactions.

Marketers and publishers continue to be wary of using personally-identifiable (PII) data
in the digital realm due to concerns about consumer privacy and data accuracy. “You
can’t make a flawless data background when moving data between multiple devices
because there are too many unknowns when it comes to privacy,” says the CEO of one
DMP company. “With addressable TV, for example, the cable distributors have PII that
they could easily match up with computer background and deliver a custom broadcast
based on a customer’s search history, but no one is willing to bridge that gap yet *in
fear of running afoul of privacy best practices+.”

As a result, data collection, analysis and segmentation processes are being driven (or
constrained, depending on your perspective) largely by the need to aggregate and then
anonymize—remove any “PII” elements—wide swaths of both first- and third-party
data. In response to this inherent complexity, many are taking a cue from the data co-
op models that emerged in the 1990s (largely for use by catalog marketers) and turning
to data exchanges, where participating digital publisher data is blended, segmented by
interests and made available to all contributors to augment their own audience
insights.
Data collection,
analysis, First-party, browser-based data—collected primarily through cookies—is being widely
modeling and supplemented with this third-party data to scale data sets and identify large,
“lookalike” audiences of high-value customers. Available sources span a wide range—
segmentation
from social media registration data (including, at times, insight into income, age and
processes are gender), to transaction-based data that includes activity on shopping behaviors, to
being driven (or general-interest data indicating news and other areas of consumer interest.
constrained,
A debate is raging, though, about the value of third-party data. Some executives warn
depending on that it is becoming increasingly generic and, therefore, less valuable. “Third-party data
your perspective) has become over-commoditized,” said an executive at one media application
largely by the developer. “We are actually seeing a shift to first-party data.”
need to
Not so, said an executive of one data technology company. “Accurate third-party data
aggregate and remains valuable because it provides context, scale and cross-channel consistency. It
then anonymize gives advertisers useful context for messaging to know the demo- and psychographic
wide swaths of elements associated with a person interested in ‘Product X.’ It provides a level of
insight-driven scale that, even in online environments, still isn’t available to advertisers
both first- and
using first-party data alone. And it is the key mechanism for reaching target audiences
third-party data. across channels with consistent messages.”

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Actions Speak Louder Than Names

Despite the challenges inherent in the PII/non-PII divide, some data executives
downplay the importance of knowing a prospect’s name and address, arguing that
pixel-driven data—insight into what an individual browser does on a website or a
platform like Facebook—often brings the sought-after targeting capabilities, even
without a consumer name. “A cookie is just as good as an individual ID,” argued an
executive at one large media-buying platform. “Knowing what people do through
trackable cookies can be very sophisticated and pinpoint those who engage or convert
at higher levels by following their behaviors—whether through display, social, a
website or viral video.”

These strategies are being enabled by large, sophisticated machine networks and
algorithms that identify useful signals and patterns of behavior that can’t be found in
PII data alone. Ultimately, many said, the consumer’s name and address isn’t as
important in raw behavioral data to determine propensity to respond. Said TruSignal’s
Liebl: “We take first- and third-party data, put it in our modeling engine and let the
algorithm decide the attributes and segmentations that identify the person as a high-
value customer.”

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Improved Operating Infrastructures: The primary barrier to widespread marketing
data optimization resides within the marketing organization, rather than the data
itself. Specifically, legacy operating infrastructures—characterized by rigid
organizational “silos” and the paucity of data-savvy marketing operations, IT and sales
talent—are substantially hindering the maximization of data, processes and systems.

“A big hurdle is how companies are organized and structured. Traditional marketing
data is managed in marketing operations, behavioral data is probably with a VP of
digital marketing and then digital data will be fragmented across those groups or have
its own VP of social media.” – John Zell, VP, Global CRM Solutions, Razorfish

Many enterprises suffer from an embedded culture of “traditional” media
management; even though they may be deploying new digital channels (as led by
distinct planning, creative and delivery teams), they are often managing those distinct
efforts in organizational silos, separate and distinct from the company’s other
marketing channels and data sources.

Panelists reported that it’s uncommon for online and offline channel managers to
share data, and typical for different managers to oversee digital execution channels
such as email, social and search. Moreover, many organizations still rely on their
corporate IT function—which commonly has neither the budget nor the decision-
making authority to steer marketing programs—to manage granular marketing data
applications. In addition, installed legacy systems and architectures (frequently built by
different contractors with the intent of making integration with other platforms
difficult) can’t accommodate the number of channels and volume of data now
available, much less the need to integrate complex, real-time data feeds.

The answer that many forward-thinking companies have developed is to invest in the
development of a data accessibility culture–led by a chief data officer. “Ultimately
there’s going to be a chief data officer (CDO) that exposes the data to you and wrestles
away some of the technology needs from the internal groups,” said Christian Ward,
senior vice president at Infogroup.
“It’s a rare breed Advertising Sales Reps Lack Critical Technology Expertise
of person who
can understand “It’s a rare breed of person who can understand what’s going on technology-wise and
what’s going on tie it to the marketing world.” – Ari Buchalter, COO, MediaMath
technology-wise The second organizational challenge that has limited the monetization opportunities
and tie it to the linked to marketing data is a lack of sales expertise when it comes to data-driven
marketing advertising. Media sales reps, for example, are historically trained to sell inventory by
way of traditionally volume- and demographic-driven variables—estimated magazine
world.”
circulation, say, or television ratings. But to successfully sell “audiences” (as defined by
Ari Buchalter, COO disparate data sources) across channels, reps today must be technology savvy as well
MediaMath as media savvy. “Reps have to get in the dirt more to understand this new ad

14


technology, and most don’t have a technical background,” explained Dwight Green,
Nielsen’s vice president of digital product leadership.

According to some industry executives, the most effective data-driven sales reps are
coming from the analytics sector because they know how to sell software as a service.
Additionally, staff with digital agency or DSP experience (reflecting an understanding of
trading desks and technology-driven data-use models) will be valuable, as they
understand the specialized buying of data-driven audiences.

Publishers built on traditional advertising sales are using education and training to
better prepare their sales forces to monetize data value through media. “The first thing
we’re doing is building a ski slope of analytics tools that include beginner, intermediate
and expert proficiency levels,” says one broadcast and online publisher. “We’ll build
out the simple tools first and invest in training and education to make it successful.”

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A strong network of data-centric technology and service partners: The fastest and
most efficient “big data” aggregation, analysis and throughput solutions require a
strong ecosystem of service- and technology-enabled partners. The burgeoning data
supply chain is proving to be extremely agile in streamlining and processing data for
marketing performance—supported by a new class of open-source tools (such as
Cassandra and Hadoop) as well as maturing data optimization providers that offer
new solutions for managing and redeploying large quantities of disparate data
sources.

“When you’re in a fast-moving environment and you want to create smaller segments,
that’s a machine-solved problem rather than a human-solved problem, and that’s the
problem we’re trying to solve.” – David Soloff, CEO, Metamarkets

“As these machine-learning processes become bigger and more important they will
need to be outsourced. More sophisticated data processing requires more exotic
software that is hard to master independently.” – Stewart Allen, CTO, Clearspring

There’s widespread industry agreement that achieving optimal data collection, analysis
and throughput performance requires a strong ecosystem of technology-enabled
partners, particularly as digital data—which is growing increasingly temporal (or time-
sensitive)—requires faster processing and integration.

The data ecosystem is proving extremely agile in the streamlining and processing of
data for marketing outputs, particularly on the demand side. Most DSPs, for example,
The “DMP have built trading desks that drive speed and efficiency in ad bidding and buying. A
approach” is corps of analytics-focused marketing agencies—grounded in data segmentation, but
grounded deeply often tasked with the execution of those strategies, as well—has emerged to drive
sophisticated audience modeling. And not to be left behind, email service providers
in a service- (ESPs) are adding more analytical services, including A/B testing, to improve their
driven supply clients’ targeting efficiency.
model,
distinguished by Other service-driven vendors, including agencies and data management platforms
(DMPs), have focused on analytics and segmentation to make data more usable for
the overlay of client marketing and advertising. As it matures, for example, the DMP market is
data access, progressively splintering into a number of primary specialty disciplines—focused
analytics and respectively on the aggregation of third-party data and intersection of ad network
technology, as well as “pure-play” models focused around the integration of customer
media data, with a variety of views into the underlying data.
optimization
capabilities (but What providers in all these groups share is a focus on integrating multichannel data
also, some streams to plug into CRM and other systems to provide data owners with a “360-
degree” view of their customers (and customer interactions). This “DMP approach” is
criticize, by a lack grounded deeply in a service-driven supply model, distinguished by the overlay of data
of core data access, analytics and media optimization capabilities (but also, some criticize, by a lack
management of core data management capacity).
capacity).

16


Technology, meanwhile, is rapidly growing to meet the critical execution
requirements—including rapid throughput, low latency and high-capacity processing
power—that real-time marketing execution demands. New open-source tools such as
Cassandra and Hadoop, for example, provide “virtual” platforms for managing
unwieldy data sets.

Marketing data governance: Data governance has emerged as a critical priority for
virtually every player in the data ecosystem. But whereas organizations have long
employed policy experts to advise on the regulatory ramifications of data utilization,
many are coming to see marketing data governance—defining the “rules of the road”
for assigning distinct data sources to different promotional tasks—as an equally
critical go-forward priority.

Realizing potential value from a vast new array of data sources presents a series of
challenges wholly separate from those associated with process management,
technology or marketing strategy. By comparison, the basic governance questions
associated with data usage— dictating who may access a given data set, and what rules
or rights to data usage, data privacy and data security may be associated with its
deployment—are just as thorny, and present an even costlier potential array of risks.

17


When it comes to umbrella data governance strategy, panelists were united on one
best practice: Unless associated with one’s own customers or other first-party data
sources, PII data is essentially off-limits for online targeting purposes. The potential
cost of doing otherwise—of running afoul of privacy regulation, or violating the
consumer’s inherent right to choice in managing his or her marketing
communications—are simply too great for most marketers to bear.

“We check with our privacy counsel Requirements of the DAA Self-Regulatory
before we do anything,” said Nielsen’s Principles for Multi-Site Data:
Green. “The penalties are tough and you
must have the right internal teams and  Organizations that collect multi-site
know the laws and acceptable standards data for purposes other than online
that are in place.” For many data owners, behavioral advertising must provide
the solution is to house data internally— transparency and control regarding
“behind the corporate firewall”—even Internet surfing across unrelated
when third-party solutions are used for websites
the purpose of managing data processing,  The collection, use or transfer of
Internet surfing data across websites
analytics or optimization.
for determination of a consumer’s
eligibility for employment, credit
For its part, the marketing data industry is standing, healthcare treatment and
moving to develop, publish and promote a insurance are prohibited
series of universal data-use guidelines in  Organizations must comply with the
an effort to provide self regulatory Children’s Online Privacy Protection
solutions that may assuage consumer or Act (COPPA) regarding the collection
regulatory concerns. The new principles— and use of children’s data
like the Digital Advertising Alliance’s  The Multi-Site Data Principles are
recent Self-Regulatory Principles for Multi- subject to enforcement through strong
Site Data—build upon FTC accountability mechanisms.
recommendations regarding the collection
of Web viewing data and establish a clear framework governing the collection of online
data that also provides consumer choice for the collection of such data.

Data transparency is a critical component of the solution. Industry executives agree
that consumers need to understand how their data is being used before they will begin
to trust brand use of that data. The preferred response for most marketers is to allow
consumers to opt out of some data use practices.

Individual vertical industries, too, are moving to balance their own unique marketing
concerns with the lucrative potential of new data sets and potential consumer
concerns about the use of that information. In the auto industry, for example—where
“data,” for example, could conceivably include detailed information on everyday
consumer whereabouts—the importance of maintaining best practices in all regards is
incredibly important. “The connected car will have a huge impact on our industry,” said
Paula Skier, senior product marketing manager for digital products at Polk. “Through
the combination of in-vehicle technology and smartphones, cars can be the conduit for
creating unbelievable amounts of data—driver and passenger attributes, driving
patterns, location, speed, media consumption, communication with other consumers

18


and even social networking. But with access to this information, [the industry] has to
demonstrate a benefit to consumers so they are comfortable with and want to
participate in programs leveraging that data.”

Global Marketers Face Restrictive Data Use Environment Abroad

With the expansion of the global marketplace, U.S.-based companies with international
operations face greater privacy and data governance challenges. For example, each
European Union country has its own set of data regulations that, individually and
collectively, are more restrictive than their counterparts in the U.S. For example: “An IP
address is PII in every country except the U.S,” argued Catalina’s Williams.

The result is that execution of data-driven marketing abroad is even more difficult.
Vigilant awareness and compliance with data regulations within each country will be
critical as the industry continues to evolve. Such interactions could be self regulated as
U.S.-based data or governed under safe-harbor rules.

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Conclusion
The use of marketing data is evolving as rapidly as the technology driving it.

Today’s immature use cases will become tomorrow’s standard marketing practices.
Strategy will follow technology, as new suppliers push the marketing envelope to
identify and integrate offline and online data streams into broader data assets that can
be analyzed, segmented and modeled—creating audience profiles that cut across
channels.

Core direct marketing skills and practices in data analysis, segmentation and modeling
will continue to provide a solid foundation for emerging digital data use cases—but
must be augmented to account for new techniques and skills required to collect,
analyze, integrate and derive value in the face of these new applications. Meanwhile,
marketers and publishers will continue to grapple with a number of challenges posed
by big data: storage capacity and accessibility; machine-generated insights (i.e.
modeling and algorithms) versus human intuition and skill; consumer choice; and the
role of PII in digital marketing.

But there will be more growth opportunities, as well, as the relationship between top-
of-funnel branding and bottom-of-funnel conversion programs become better defined
in the online world. There’s widespread agreement among marketing industry
executives that consolidation is coming—and that it will encourage more brand
marketers and publishers to mature and grow their deployment of data use cases (and
maybe acronyms, too).

“Within the digital ecosystem we’ll start to see consolidation and horizontal
integration,” said Caribou Honig, a partner with QED Investors. “Point solutions
focused on a single channel will fall to the wayside unless they’re highly superior, and
even then they’ll be integrated with a platform somewhere. Ultimately, your online
display DSP, online video DSP, social DSP and PPC platforms will all reside on a single
platform.”

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Appendix A: Our Research Panelists Tell Us What
“Data Developments” They Expect to See as 2012 Unfolds
“Attribution and cross-channel performance aggregation will continue to expand and
become utilized to greater marketing benefit.”

“More advanced machine-learning techniques that incorporate meaningful data
points to predict outcomes. The algorithms are out there, we just need to plug in all
the disparate data sources—context, first- and third- party data, site, geography—to a
stable online ID pool in real time to deliver the right creative to the right person and
the right place for a brand to pinpoint the best prospect.”

“We’ll continue advancing in connecting multichannel marketing and personalization
via a host of services and technologies. In a short period of time, consumers will more
strongly voice preferences on how they choose to accept marketing messages.
Marketers quick to adapt to these preferences will pull away from the pack.”

“The metrics for display advertising need to change. Basic clickstream metrics provide
little to no insight into success/failure. Additionally, a large percentage of online
targeting through multiple platforms will be driven by data on the front end.”

“The convergence, with sufficient anonymization, of large offline data segments into
online platforms. It is an untapped resource and data companies and CRM marketers
are becoming savvier about the opportunities.”

21


“Offline data is valuable and will bring the tried-and-true maturity of offline market
research and advertising lessons learned to the digital space. It will bring consistency
and scale back into the multichannel advertising equation.”

“The willingness to rework frameworks—especially in the area of offer management—
to reach customers and potential customers effectively and efficiently. Most
advertising agencies are unwilling to recommend this to their clients because it will
ultimately result in loss of revenue.”

“The combination of qualitative and quantitative measurement. Large companies
aren’t yet driven to ask how positive or negative sentiment reflects upon other
numbers. Where are the dependencies? How can this be measured and acted on?”

“The sheer volume of data available will require marketers and their respective
channels and vendors to be able to digest and deal with “big data.” Those who have
access to larger data pools will be exponentially better equipped and can significantly
build out market share.”

“With more robust offline data able to be connected at a sub-zip code level as a geo-
targeting technique, the use of single-threaded cookie attributes as the definitive
targeting methodology will fade. Inferred interest as a sole metric will fade and so
could the privacy issues associated with tracking users.”

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Appendix B: A Marketing Data Lexicon
Evolving technology, data and marketing process are ushering in an entirely new
language to define how advertisers, publishers and intermediaries do their work. The
below lexicon—developed predominantly by the IAB Networks and Exchanges
Committee—represent a small selection of the terms appear that appear in this paper
and may be new to various constituencies of the advertising ecosystem:

Term Definition
Ad Network Provide an outsourced sales capability for publishers and a means to
aggregate inventory and audiences from numerous sources in a single
buying opportunity for media buyers. Ad networks may provide
specific technologies to enhance value to both publishers and
advertisers, including unique targeting capabilities, creative generation
and optimization. Ad networks’ business models and practices may
include features that are similar to those offered by ad exchanges
Cookie A small text file sent by a website’s server to be stored on the user’s
Web-enabled device that is returned unchanged by the user’s device to
the server on subsequent interactions. The cookie enables the website
domain to associate data with that device and distinguish requests
from different devices. Cookies often store behavioral information
Data Management A technology-enabled infrastructure for managing the aggregation,
Platform (DMP)* integration, analysis and redeployment of multiple first- and third-party
data sources, particularly online
Demand Side Provide centralized (aggregated) media buying from multiple sources
Platform (DSP) including ad exchanges, ad networks and sell-side platforms, often
leveraging real-time bidding capabilities of said sources. While there is
some similarity between a DSP and an ad network, DSPs are
differentiated in that they do not provide campaign management
services, publisher services nor direct publisher relationships
First-Party Data* That which is sourced by, owned and managed by an entity (or its
direct affiliates on its behalf) independently
Personally User data that could be used to uniquely identify the consumer.
Identifiable Examples include name, social security number, postal address and
Information (PII) email address
Pixel (or Beacon) An HTML object or code that transmits information to a third-party
server, where the user is the first party and the site they are interacting
with is the second party. Pixels are used to track online user activity,
such as viewing a particular Web page or completing a conversion
process
Segment A set of users who share one or more similar attributes
Third-Party Data Data that did not originate from either the publisher or advertiser.
Typically this is used to enhance ad targeting. For example,
demographic data from a third party might be used to help determine
which auto ad (make/model) to display on an auto site
* Defined by Winterberry Group

23


A global leader in interactive marketing services, Acxiom Corporation connects clients
with their customers through deep consumer insight that enables profitable business
decisions. We incorporate decades of experience in consumer data and analytics,
information technology, data integration and consulting solutions for effective
marketing across digital, Internet, email, mobile and direct mail channels.
Headquartered in Little Rock, Ark., Acxiom serves clients around the world from
locations in the United States, Europe, Latin America and the Asia-Pacific.

For more information, please visit www.acxiom.com.

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visit thinking.netezza.com.

24


The Interactive Advertising Bureau (IAB) is comprised of more than 500 leading media
and technology companies that are responsible for selling 86 percent of online
advertising in the United States. On behalf of its members, the IAB is dedicated to the
growth of the interactive advertising marketplace, of interactive’s share of total
marketing spend, and of its members’ share of total marketing spend. The IAB
educates marketers, agencies, media companies and the wider business community
about the value of interactive advertising. Working with its member companies, the
IAB evaluates and recommends standards and practices and fields critical research on
interactive advertising. Founded in 1996, the IAB is headquartered in New York City
with a Public Policy office in Washington, D.C.

For more information, please visit www.iab.net.

Winterberry Group is a unique strategic consulting firm that helps advertising,
marketing, media and information companies build value. Our services include:

Corporate Strategy: The Opportunity Mapping strategic development process
prioritizes customer, channel and capabilities growth options available to advertising
and marketing industry firms, informed by a synthesis of market insights and intensive
internal analysis.

Market Intelligence: Comprehensive industry trend, vertical market and value chain
research provides in-depth analysis of customers, market developments and potential
opportunities as a precursor to any growth or transaction strategy.

Marketing System Architecture: Process mapping, marketplace benchmarking and
holistic system engineering efforts are grounded in deep supply chain insights and
“real-world” understandings—with a focus on helping marketers and publishers better
leverage their core assets.

Mergers & Acquisitions Due Diligence Support Services: Company assessments and
industry landscape reports provide insight into trends, forecasts and comparative
transaction data needed for reliable financial model inputs, supporting the needs of
strategic and financial acquirers to make informed investment decisions and lay the
foundation for value-focused ownership.

For more information, please visit www.winterberrygroup.com.

25

searching for
balance in the use
of personal data
the yin-yang of 21st century commerce

Acxiom Corporation | 601 E. Third, Little Rock, AR 72201 | www.acxiom.com •
•1
For Acxiom’s view on privacy, visit www.acxiom.com/privacy
© 2012 Acxiom Corporation. All rights reserved.

Executive Summary
Data is neither good nor evil but rather a facilitator of our modern way of life. It drives
commerce, creates jobs and helps people live longer, more rewarding lives. Yet, bad
actors, questionable uses of the data and the occasional one-sidedness of the dialogue
cloud the public’s view. This document presents a case for a balanced approach through
which both organizations and individuals benefit enormously.
Achieving balance in using personal data requires a comprehensive commitment to
responsible use. Here, Acxiom provides a detailed description of what the balanced
world of data looks like, as well as our commitment to its standards.
We also know many are curious about what Acxiom does with data. This paper
addresses many common questions and misperceptions about data and our business;
and it provides avenues for an open and balanced discussion. In this time of incredible
change and innovation, we welcome the dialogue.

searching for balance in the use
of personal data
the yin-yang of 21st century commerce

Question: what do taxes, credit, the census, photography, telephones, the
Internet, mobile communications, surveillance cameras, GPS and marketing all
have in common?
Answer: in one way or another, each has sparked questions about what’s
proper or improper in the use of personal data.

In the dynamic relationship between commerce, technology and privacy, data’s role in serving
humanity has evolved and expanded over the years (see sidebar: Through the Ages.) With each
technological or business innovation regarding the use of data, important questions always emerge:
Do individuals benefit? Are lives enriched? Is society better off? What are the costs vs. the benefits
of regulation?
It is an eternal, evolving yin and yang: not opposing forces but complementary, dynamic needs that
interact within a greater whole.
However, today’s dialogue about data use can often be one-sided, underpinned by naiveté, profit
motives, mistrust or misperceptions. That’s unfortunate. The use of data can benefit all of us in
many ways. It can make life more fulfilling and can advance societal good; but data use also poses
legitimate concerns and public policy questions.
We do know this: data is not good or evil, moral or immoral — it is increasingly a product, a facilitator,
of our modern way of life, and its importance to both individuals and organizations is intensifying.
•1•

“... data is not good or evil, moral or
immoral — it is increasingly a product,
a facilitator, of our modern way of life”
Therefore, the dialogue about data use should be open, calm and holistic. Present and future
discussions regarding the use of personal data should seek balance … a balance to serve the
intersecting needs of the people who live on this planet, the commerce between them and the
requirements of their societies.
Acxiom is part of an industry that has served both commerce and consumers for more than 100
years. In this paper, we will discuss the balance between the use of personal data and privacy,
address some misconceptions, and share our commitment and perspective. Please join the
discussion. We welcome your questions and input.

The Engine of Modern Commerce: Vitamin D
Say it fast three times: “hypervitaminosis D.” It is a rare, but potentially serious condition occurring
when you have excessive vitamin D in your body.
Vitamin D is also the “happiness vitamin.” It fosters healthy bone growth and maintains the normal
functioning of the nervous system. When you are in sunlight, you absorb vitamin D, and it in turn
makes you happy. Too little can depress you, but too much is not a good thing either.
Natural ingredients found in grape skins and therefore red wine can prevent heart disease and help
destroy pancreatic cancer, but too much red wine can lead to a host of ills. Overindulging in water,
food or oxygen is equally problematic, but without each of them, we don’t live.
Our world needs balance.
So it is with data, the vitamin D of business; it too requires
balance. Data is the backbone of modern commerce,
creating jobs and economic growth. Data and advertising
fund much of our entertainment. Government needs
data to keep citizens safe. Your health provider needs
coordinated data to help you live a longer, richer life.
Would Facebook be Facebook if your friends couldn’t
find you? The list of ways data enriches business and
humanity is endless.
But, not all data use is appropriate, and what is
permissible has changed over the years. Data used for
marketing purposes has a very different impact than
data used for granting credit or determining eligibility
for health insurance. And, what is personal is not
always private; in fact, much personal information is
already a matter of public record, and with every new
technological innovation, new questions arise.
Today, consumer advocates, non-profit, business and
government leaders are considering the appropriate
use of health records, location information and
Internet cookies, among other forms of data.

•2•

In blogs, white papers, books and discussions around the planet, we ask, “Are there uses of data
that are actually harmful? Who owns all this data? How do we decide what is appropriate for some
without restricting benefit for others? How do we evaluate the cost-benefit tradeoffs?”
In 2011, McCann Truth Central reported
on a study conducted across 12 global listen
Only when we
markets to understand what privacy means
to the average consumer.1 “What emerged as much as we talk,
was a new understanding of the privacy
issue: yes, consumers are concerned
and collaborate,
about privacy, but privacy is a complex, will we “see” the full
multi-dimensional issue that encompasses
everything from personal, real-world elephant for
snooping to sharing data online. When it what it is.
comes to data sharing one must unpack
the issue even further as consumers
categorize data into different categories,
e.g., shopping, location, personal, medical
and financial, and have varying degrees of
concern with sharing each type.
“In fact, 71% of consumers indicate they
are willing to share shopping data with a
brand online. 86% of consumers see that
there are major benefits associated with
sharing data with businesses online, and
65% see one of the top two benefits as
better access to discounts and promotions.”
Laura Simpson, Global Director of McCann
Truth Central, said of the study, “… we found that
consumers are in favor of sharing shopping data
with businesses in exchange for certain benefits
but are more cautious about sharing financial and
medical data.” She continued, “While the foremost
concern must be to protect the data and privacy
of customers, a smart strategy also encourages
responsible sharing of relevant data, benefiting
both the brand and the consumer.”
Yet, the occasional one-sidedness of
the dialogue has created unproductive
misconceptions such as “marketing services companies collect and sell data to anyone, provide
private information to governments, spy on individuals, track their movements, are creepy, evil,
terrifying and frightening.” One nationally syndicated blogger used the words, “snooping” “sneaky”
“bad” “demon” “shadowy” and “slurping” all in one subtitle.
Without an open dialogue, it is no surprise there is mistrust and suspicion. Like the blind men and the
elephant in the ancient parable, we stand around this topic describing it from our own point-of-view
in complete disagreement. Only when we listen as much as we talk, and collaborate, will we “see” the
full elephant for what it is.

•3•

Commerce, Technology and Privacy:
Through the Ages
1878 Alexander Graham Bell installs the first telephone exchange in New Haven, Connecticut

1890 The Harvard Law Review publishes Louis D. Brandeis and Samuel D. Warren’s article,
“The Right to Privacy” questioning the potential invasion of privacy by the telephone
and candid photography

1936 Social Security numbers are assigned to most adult Americans

1957 Russia puts Sputnik, the first artificial satellite, into Earth’s orbit, leading the way
for worldwide satellite-based communications and observation

1970 The Fair Credit Reporting Act (FCRA) regulates collection, dissemination and use
of consumer information

1971 Direct Marketing Association’s (DMA) Mail Preference Service is created to help people
filter direct mail marketing

1980 Organization of Economic Cooperation and Dev. (OECD) issues guidelines on the protection
of privacy to “harmonize national privacy legislation and ... prevent interruptions in
international flows of data”

1982 Federal Communications Commission (FCC) authorizes commercial cellular service
for the U.S.

1989 World Wide Web service available on the Internet

1996 Health Insurance Portability and Accountability Act (HIPAA) addresses the security and
privacy of health data

2003 U.S. establishes the first national standards for the sending of commercial e-mail and
requires the Federal Trade Commission (FTC) to enforce its provisions (CAN SPAM)

2003 U.S. establishes the FTC’s National Do-Not-Call Registry in order to facilitate compliance
with the Telephone Consumer Protection Act of 1991

2004 Facebook debuts

2009 The Online Behavioral Advertising (OBA) Privacy Principles, the industry’s most comprehensive
guidelines on privacy and the collection and use of user data, is jointly released by
The Interactive Advertising Bureau (IAB), American Association of Advertising Agencies (4A’s),
Association of National Advertisers (ANA), Direct Marketing Association (DMA), and the
Council of Better Business Bureaus (BBB)

2011 In response to FTC urging, online advertisers and websites form the Digital Advertising
Alliance and establish guidelines and capabilities to allow users to opt out of having their
online activities tracked

2011 Commercial Privacy Bill of Rights proposal tasks the FTC with developing rules requiring
companies to offer consumers “a robust, clear, and conspicuous” choice mechanism

•4•

The Good, the Bad, the What Were They Thinking
Marketing, and the data that informs it, is the engine of commerce, creating economic growth and
jobs around the planet. While research presents mixed opinions on the use of data for marketing,
individuals actually respond far more favorably to data-fueled advertising that meets their specific
needs … some studies show three times more favorably than generic approaches.2
People expect marketers to deliver messaging that is relevant and engaging. In fact, they’re annoyed
when it isn’t. The digital world in which we live and shop amplifies this effect; for example, people
sign up for the Do-Not-Call list in order to reduce unwanted telemarketing calls. However,
opting out from data-fueled online advertising in the same way has a very
different effect: advertisers just present a lot more untargeted ads.
In their 2010 study, Goldfarb and Tucker3 found online
advertising effectiveness dropped by 65 percent in
Europe when restrictions on targeted ads became
more rigorous. Advertiser response? More ads, and
more ads with “interactive, audio or visual features.”
In the researchers’ words, “we suggest that as the
use of customer data by marketers online becomes
increasingly regulated, ads may become more
obtrusive.”
Accomplishing the relevance we are describing,
of course, requires data. On the commerce side,
particularly regarding digital commerce, the overall
positive impact of using data has been profound:
• In a 2009 study commissioned by the Interactive
Advertising Bureau (IAB) and produced by
Harvard Business School professors John
Deighton and John Quelch, they calculated that $300 billion in
U.S. economic activity and 3.1 million jobs are generated by the advertising-
supported Internet.4
• In a 2010 study by IAB-Europe, the researchers found that people in the U.S. and Europe derive
significant value from ad-funded Web applications — more than had been thought. In fact,
advertising effectively finances a consumer surplus of approximately €100 billion for 2010 in the
U.S. and Europe, and this number is expected to grow at a double-digit rate.5
• In 2011, analysts at McKinsey & Company calculated that data could unlock $300 billion in
potential annual value to U.S. health care, and that the U.S. would need 140,000 to 190,000 more
analytical talent positions and 1.5 million more data-savvy managers because of it.6
But the value of data, even digital data, is not limited to commerce. The World Economic Forum
reports on two examples in its report entitled Big Data, Big Impact: New Possibilities for International
Development:7
• “In the wake of Haiti’s devastating 2010 earthquake, researchers at the Karolinska Institute and
Columbia University demonstrated that mobile data patterns could be used to understand the
movement of refugees and the consequent health risks posed by these movements. They were
able to analyze the destination of over 600,000 people displaced from Port-au-Prince, and made
this information available to organizations dealing with the crisis. Later that year, when a cholera

•5•

outbreak struck the country, aid organizations used this data to prepare for new outbreaks. The
example from Haiti demonstrates how mobile data analysis could revolutionize disaster and
emergency responses.”
• “The San Francisco-based Global Viral Forecasting Initiative (GVFI) uses advanced data analysis
on information mined from the Internet to identify the locations, sources and drivers of local
outbreaks before they become global epidemics.”
Many live richer, more rewarding lives because business, public service and not-for-profit
organizations responsibly use personal data. Need more examples? In recent years, the use of data
has helped people:
• Live longer, fuller lives, through proper coordination of health information
• Find true love — one in six marriages in the U.S. originates from online dating services8
• Promote freedom of speech and the unencumbered expression of ideas
• Be safer by making it easier to root out and identify bad people (criminals, sex offenders, etc.)
• Keep personal finances safer and provide alerts if someone has attempted a theft
• Enjoy free entertainment and content, funded by data-fueled advertising
• Capitalize on greater choice in products and services (generally lowering prices as well)
But just as clearly, there have been inappropriate uses. There are purposeful bad actors and those
who have simply ignored the little voice that says, “This doesn’t feel right.” Others have not made
data security and privacy a high-enough priority.
• A leading advertising technology company copied mobile subscribers’ entire address books without
their knowledge
• Another leading technology/media company overrode browser preferences in favor of its own
• Major hospitals in some of the largest cities in the U.S. made medical records of celebrities
available to the media
• A U.S. government department released Social Security numbers of tens of thousands of living
Americans in a widely available database of dead persons intended to protect U.S. businesses from
fraud.
There are many more of these bad actions — too many to list. The question is how do we find
a balance? What should business and not-for-profit organizations, governments, advocates
and individuals do to keep the commerce, technology and privacy balance viable? What are the
principles?

•6•

“many live richer, more rewarding
lives because business, public service
and not-for-profit organizations
responsibly use personal data.”

Another challenge is determining whether or not personalization in online advertising and news
creates an artificial and unfair view of the world to different audiences. Researchers and authors have
referred to these as “filter bubbles”9 and “echo chambers.”10 One of the authors, Farhad Manjoo, has
since reversed his view based on a “Facebook study [that] is one of the largest and most rigorous
investigations into how people receive and react to news.” In Manjoo’s words, “… I’m gratified by
Bakshy’s [Facebook] study.11 The echo chamber is one of many ideas about the Web that we’ve
come to accept in the absence of any firm evidence. The troves of data that companies like Facebook
are now collecting will help add some empirical backing to our understanding of how we behave
online. If some long-held beliefs get overturned in the process, then all the better.”
We applaud Manjoo and others like him who support objective investigation over self-serving
sensationalism masquerading as advocacy.

Can We Do This Together?
The commerce-technology-privacy challenge does not rest on one segment of society. It isn’t an
isolated challenge. It has existed through much of our history and has expanded over time.
Governments should make data security legislation a priority — look after the interests of all parties
in this debate: technology must progress, businesses and not-for-profits must be able to serve
customers and donors effectively, and individuals must have choices about how personal information
related to them is used.
Privacy advocates, journalist and bloggers should continue their vigilance about protecting
individuals’ ability to choose how personal data is used. In addition, they should continue to advocate
for effective data security.
Acxiom will continue to help marketers turn data into actionable insights in a responsible fashion (see
our commitment below.) Thus, companies will be able to develop offerings aligned with individual
interests; and people can engage with companies, brands and products in a way they prefer. All
parties benefit.
And what should each of us as individuals do? We should urge our elected representatives to
understand the need for balance between our privacy and our need for convenience and robust
commerce. We should understand our rights regarding how information about individuals is used. We
should be vigilant about data security at home and work, and we should explore the opportunities to
tune the relevance of our individual data footprints within business and not-for-profit organizations.

•7•

What does balance look like?
There is a huge opportunity for personal data, when used in a responsible fashion, to drive commerce
and to make lives easier, safer and healthier. But there are legitimate public policy issues. What are
the business principles that should guide the use of personal information? For businesses using data
about individuals for marketing purposes, we believe there are several:
• Security – make data security a priority. Implement and maintain robust processes and programs
for ensuring appropriate monitoring, detection and resolution of potential issues.
• Choice – provide choices for the use of personal data; either opt-out or opt-in options depending
on the type of data, intended use and regulations.
• Don’t be creepy – here’s a litmus test: are your actions for the individual (not creepy) or to the
individual (creepy.) A creepy movie, story or experience is usually about the unknown, the hidden
motivation, the ulterior motive. Be as open as you possibly can about your interactions with
individuals; use data responsibly to help the individual; provide descriptions of your processes; and
describe how you ensure personal data is kept safe.
• Transparency – (related to not being creepy) be clear about what data you capture, how it’s used
and with whom you share it.
• Compliance – comply with regulations and industry guidelines. Avoid marketing to inappropriate
segments of the population, and do not market inappropriately to vulnerable segments.
• Relevance – serve individuals with highly relevant and engaging content based on individual
tastes and needs. Understand and act on explicit individual preferences.
• Optimize to true long-term customer value – detailed information about individuals is often
the most valuable in marketing scenarios yet presents the most sensitive privacy questions. Don’t
be tempted to the quick-buck-dark-side by prioritizing short-term results over long-term value
creation. Use data appropriately to build trust-based relationships, not quick scores.

“here’s a litmus test:
are your actions
for the individual
(not creepy) or to the
individual (creepy.)”

•8•

Acxiom’s Commitment
Acxiom is committed to the appropriate use of data. We
actively participate in conversations about data, working
hard to create policies that both protect individuals and allow
for the responsible use of data by business, public service
and not-for-profit organizations. And we regularly advise
our clients, vendors and partners on the responsible use of
personal information.
We also want to be very clear that the focus of our business
is marketing. We do NOT permit our clients to use Acxiom-
provided personal data to make hiring decisions, make
insurance underwriting decisions or to help credit providers grant or deny credit.
Data security remains an essential priority for us — we have a robust program to drive appropriate
monitoring, detection and resolution of potential issues. Our security controls include vulnerability
and penetration testing programs; firewalls and malware protection; and mandatory annual security
awareness training for all employees.
Acxiom’s data consists of publicly available information, permissible public record information,
information from surveys and data from other providers. While all of our data collection complies
with laws and industry best practices, our marketing data adheres to an even higher standard.
One example is that we review our marketing data suppliers’ online privacy policies to determine if
individuals are provided notice that information will be shared for marketing purposes and that people
have a choice about such sharing. We do not work with data suppliers whose policies do not meet
our strict standards.
Acxiom does not collect cross-domain web browsing activity, but we do work with clients and
partners who wish to use web browsing activity and our data to present more relevant advertising.
When we enable this, we comply with all applicable laws and the higher standards established by
industry trade associations like the Direct Marketing Association, the Interactive Advertising Bureau
and the Digital Advertising Alliance.
Acxiom provides to our clients three types of data — data that helps companies market more
effectively, data that helps reduce fraud and identity theft, and data that helps people find businesses,
public services, not-for-profits or other people through our directory services. For the last two
categories, we allow individuals to see and correct the data we have.
Because data security is a priority, allowing individuals to access and correct the data for the first
category — data that helps companies market more effectively — is complex and sensitive. It’s
important to note that marketers and individuals have different needs for data. While people want
to see individual data related to them, marketers want to see and act on that information in volume.
Marketers combine this information into segments that make their campaigns practical and cost-
effective. So, from the marketer’s point of view, there’s no need to extract information on a single
individual.
Consequently, we don’t have a system to accommodate this. However, we know people are curious
about the marketing data Acxiom has. We think constantly about the future of marketing data and
these kinds of new offerings may appear in our future product releases, but only if we can provide the
proper security, navigability, understandability and system scalability. We simply cannot allow private
information to be exposed to individuals or organized cyber-threats.

•9•

In reality, more robust “notice and choice” capabilities would mitigate, to some extent, the need for
access. In the future, notice and choice might be facilitated in similar fashion to the email filtering
many of us use today; for example the option to “Click here to download pictures” in Outlook.
Our clients and all affected individuals can expect Acxiom to continue to be a thought leader on this
important topic. If you have a question, send it to Ask_Acxiom@acxiom.com and we’ll do our best to
answer it.
Please download the information here to learn more about the data we have and how it is used. In
addition, our U.S. Products Privacy Policy further explains how we collect and use data. Or, you can
choose to opt out of our marketing data completely.
We are continuing to help our clients with contact suppression services (such as “do not call,”
“do not mail” and “do not track”). This helps them comply with regulations and industry guidelines,
enhances their marketing performance, increases ROI and may lower their impact on the environment
by helping them recognize individuals who have exercised their choice and opted out. It also
recognizes individuals for whom a campaign may be inappropriate, such as under-aged, deceased, in
prison, etc.
Finally, we will always strive for greatest transparency possible. In 1991, we became one of the first
companies to post a comprehensive privacy policy. We have granted hundreds of media interviews
around the world and will continue doing so. We will openly advise government and business leaders
about effective ways to protect privacy. Nevertheless, we are sensitive to and respect the need for
our clients to remain competitive and take advantage of the information economy we all enjoy.
We are convinced that having data is not inherently good or bad. Certain uses of data can create risk
for individuals, and those risks must be minimized, but data clearly provides tremendous good for the
economy, for jobs and for individuals.
We commit to seek the appropriate balance — always.

Spectrum of Consumer
Attitudes Toward Privacy
Looking at the spectrum of consumer attitudes
toward privacy, McCann Truth Central
identified five distinct segments:
Eager Extroverts (15%),
Sunny Sharers (20%),
Savvy Shoppers (37%),
Cautious Communicators (9%)
and Walled Worriers (19%).

• 10 •

Additional Reading
Everyone is entitled to his or her opinion. Passionate journalists, privacy and consumer advocates,
business and not-for-profit leaders, governments, even technologists are all a vital part of the
balanced, open discussion of personal data and how it should be used. Academics, industry analysts,
think tanks, consultants and leaders in other industries have participated in the discussion as well.
Here are additional reading resources from many points of view:
• A search on “personal data” at the World Economic Forum provides hundreds of reference articles
• “Online privacy: Do we need ‘Do-Not-Track?’” – by Thomas M. Lenard, president and senior fellow,
Technology Policy Institute, July 17, 2012
• “What, Me Worry? The Privacy Question” – NPR blog by Alva Noë, Philosopher, University of
California, Berkeley, July 10, 2012
• “Stupid Media Watch: The Times Outdoes Itself” – Blog by Ken Magill, former DMNews reporter,
June 26, 2012
• “Big Data, Big Impact: New Possibilities for International Development” – research paper by The
World Economic Forum, 2012
• “You for Sale: Mapping, and Sharing, the Consumer Genome” – New York Times, Business Day-
Technology article by Natasha Singer, June 16, 2012
• “Big Data for the Greater Good” panel discussion facilitated by Roberto Zicari, Editor of www.
ODBMS.org and professor of Database and Information Systems at Frankfurt University, June 4,
2012
• “The Post American World” – book by Fareed Zakaria, 2012
• “Foursquare on Why Recycling Your Data is Good for You” – CNet review article by Roger Cheng,
February 29, 2012
• “Data for the Public Good” – book by Alex Howard, 2012
• “The Daily You” – book by Joseph Turow, 2012
• “The Filter Bubble: What the Internet Is Hiding from You” – book by Eli Pariser, 2011
• “In Defense of Data: Information and the Costs of Privacy” – research paper by Thomas M. Lenard
and Paul H. Rubin of the Tech Policy Institute, May 2009
• “True Enough: Learning to Live in a Post-Fact Society” – book by Farhad Manjoo, 2008

NOTE: For a January 17, 2012 retraction of sorts by author, Farhad Manjoo based on a study of
Facebook users; see “The End of the Echo Chamber”

• 11 •

Detailed Recommendations for Balance
Imagine that together we are going to prescribe a code of behavior and expectations for the
confluence of commerce, technology and privacy. Might it go something like this?

Business and Not-for-Profit Organizations should:
• Make data security a priority. They should have:
––Comprehensive programs for ensuring strict monitoring, detection and resolution of potential
issues
––Internet vulnerability and penetration testing programs backed with additional testing by third-
party experts
––Intrusion detection programs to monitor internet footprints for misuse
––Firewalls and malware protection for all internet systems and appropriate separation of internet
risks from data centers
––Mandatory annual security awareness training for all employees

• Make privacy a priority:
––Respect the privacy of every individual about whom they maintain information by providing
appropriate choices
––Comply with CAN-SPAM, Do-Not-Call and other channel-specific regulations
––Honor individual wishes by flagging or removing records from telemarketing, direct mail and email
marketing lists of people who have expressed such preferences
––Regularly advise clients, vendors and partners on the responsible use of personal information
––Actively review privacy policies of suppliers and partners to ensure the information they provide is
from appropriate sources
––Anonymize (de-identify) data whenever possible
––Only keep data as long as it has value and is accurate

• Make choice-based, hyper-relevant marketing a priority:
––Deliver marketing messages that are hyper-relevant and ultra-engaging based on individual tastes
and needs
––Develop offerings and products aligned with individual interests
––Enable individuals to engage with organizations, brands and products in the ways they prefer
––Build and manage preference centers where individuals can directly express their desires about
communication

• Make transparency a priority:
––Make it as easy as possible for individuals to stay informed about the collection, use and sharing
of personal data
––Contribute to the use of data that benefits marketers, individuals and society and if there is
conflict, support open and honest discussion and debate

• 12 •

––Openly advise and consult with government and business leaders, and with individuals
themselves, about effective ways to protect individual privacy
––Avoid marketing to inappropriate segments of the population:
––Block (suppress) contact to an individual through a specific channel or for a specific campaign
when appropriate
––Comply with regulation and industry guidelines that will enhance marketing performance by
recognizing individuals who have opted-out or for whom a campaign may be inappropriate such
as under-aged, deceased, in prison, etc.

Governments Should:
• Make data security legislation a priority (see the list above for business and not-for-profit
enterprises)
• Look after the interests of all parties in this debate: technology must progress, businesses and
not-for-profits must be able to effectively serve customers, and individuals must have appropriate
choices about how personal information related to themselves is used
• Encourage the development of industry guidelines as a means of defining appropriate and
inappropriate behavior early in the evolution of new technologies, new business models and new
data uses

Privacy Advocates, Journalist and Bloggers Should:
• Continue vigilance in protecting individuals’ ability to exercise appropriate choices about how
personal data is used
• Advocate for and prioritize effective data security (see the list above for business and not-for-profit
enterprises)
• Do as they say. Respect individual privacy by implementing the data collection policies for which
they advocate
• Seek balance, not sensationalism. Data is not good or evil, moral or immoral — it is increasingly a
product, a facilitator, of our modern way of life; its importance to both individuals and organizations
is intensifying

Individuals Should:
• Understand their rights in regard to choosing how personal information related to them is used
• Be vigilant about data security at home and work
• Explore opportunities to tune the relevance of their data footprint within business and not-for-profit
organizations; for example, Amazon enables people to manage the recommendations they make in
the area entitled, “Improve Your Recommendations.”
• Manage tracking and their online marketing footprint with tools such as Ghostery (http://www.
ghostery.com/) or Firefox Collusion (http://www.mozilla.org/en-US/collusion/)

• 13 •

Acxiom Corporation
601 E. Third, Little Rock, AR 72201
www.acxiom.com

(1) McCann Truth Central Discovers That Privacy Represents The Biggest Opportunity For Marketers Today, October 18, 2011 (2) Privacy Regulation and Online Advertising,
Goldfarb and Tucker, 2010 (3) Privacy Regulation and Online Advertising, Goldfarb and Tucker, 2010 (4) Economic Value of the Advertising-Supported Internet Ecosystem,
commissioned by the Interactive Advertising Bureau (IAB) and produced by Harvard Business School professors John Deighton and John Quelch, 2009 (5) Consumers
driving the digital uptake — The economic value of online advertising-based services for consumers, white paper by IAB Europe/McKinsey, September 2010 (6) Big data:
The Next Frontier for Innovation, Competition, and Productivity, report by McKinsey Global Institute, May 2011 (7) Big Data, Big Impact: New Possibilities for International
Development — research paper by The World Economic Forum, 2012 (8) Online Dating Statistics: How Many People Date Online? (9) The Filter Bubble: What the Internet Is
Hiding from You — book by Eli Pariser, 2011 (10) True Enough: Learning to Live in a Post-Fact Society — book by Farhad Manjoo, 2008
(11) The End of the Echo Chamber, A study of 250 million Facebook users reveals the Web isn’t as polarized as we thought — article for Slate, By Farhad Manjoo, 2012

© 2012 Acxiom Corporation. All rights reserved. Acxiom is a registered trademark of Acxiom Corporation. All other trademarks and service marks mentioned herein are
property of their respective owners.

AC-1071-12 7/12

Randy Hlavac
CEO - Marketing Synergy Inc
Lecturer Professor – Northwestern University, Medill IMC [Integrated Marketing Communications]

Randy Hlavac is a social and integrated marketing expert. In 1990, he founded Marketing Synergy, Inc
[MSI]. MSI helps business and consumer focused companies define, engage & acquire high value
communities using social, web, mobile and integrated marketing technologies. Using value based
predictive systems and marketing databases integrating social and integrated marketing channels, MSI’s
clients build profitable, long-term relationships with their most valuable market segments. Marketing
Synergy aids its clients in developing and deploying the marketing database, analytical, and marketing
systems necessary to achieve its business goals.

In addition to being the CEO of Marketing Synergy, Randy is also a professor at Northwestern. In the
Medill IMC program, Randy teaches grad and undergrad courses on social & integrated marketing. His
social marketing class was written up in the Wall Street Journal [Here Tweeting is a Class Requirement
(3/09/1)]. He also teaches web and traditional IMC marketing strategies and tactics. Prior to starting
MSI and teaching at Northwestern, Randy managed analytics and marketing teams at Mutual of Omaha,
Metromail, Experian, and TRW Target Marketing Services.

Randy is a board member for the Chicago Association of Direct Marketing [CADM] and is a frequent
speaker on social, web, and database marketing at the DMA, DMIC, AMA and other marketing
organizations. Randy is also a frequent speaker at Northwestern’s Allen Center for adult education
where he talks on social media, social monitoring, and social marketing.

Currently, Randy is a social marketing blogger and is currently completing his first book – Social IMC
Busting the Myth of Social ROI. Randy also writes articles for the Journal of Integrated Marketing,
Chicago Association of Direct Marketing and is a frequent guest blogger on social, marketing
technologies, and integrated marketing.

Randy can be reached at RHlavac@MSINetwork.com or at 630.328.9550. You can also follow Randy on
Twitter at @RandyHlavac or call him on skype at randy.hlavac

Integrating Digital Media Data with
Your Marketing Database

Randy Hlavac
Lecturer, Medill IMC Program
Northwestern University
CEO, Marketing Synergy Inc
r-hlavac@northwestern.edu
630.328.9550

Copy write 2012 Marketing Synergy Inc. Any reproduction or use without the
Marketing Synergy, Inc MSINetwork.com #DMA2012
express written permission of Marketing Synergy Inc is illegal

Social Marketing with bottom lineSOURCE: Medill IMC
impact AGE: 39 [I dye my hair gray for effect]
DIFFICULTY: 4
FIGHTING STYLE: Non-Existent
TWITTER: @randyhlavac
Cracking the code to Social ROI
EMAIL: R-hlavac@northwestern.edu
HASHTAG: #NUSocialIMC
RANDY SPECIAL MOVE: Shout of Earth
Randy
(Left, Right, Up, Down, A, A, A, B, B, B) Hlavac
RATING: Awesome
630.328.9550


The Problem is Clear …

 80% of all companies have
no idea of how to deploy
social media to generate
measurable results?


… and the need is critical

Your travelers and partners
are using it to make
purchase decisions!

… and they will be talking to
friends and influencers to
help in the decision process


What is Big Data?
• Big data is a collection of data
sets so large & complex that it
becomes awkward to work with
using on-hand database
management tools. Difficulties
include capture, storage, sharing,
analysis & visualization
• Big data is data that exceeds the
processing capacity of
conventional database systems.
The data is too big, moves too
fast, or doesn’t fit the strictures
of your database architectures
O'Reilly Radar (http://s.tt/1kHFU)


Big Data – According to IBM
Big data spans four dimensions: Volume, Velocity, Variety, and Veracity.

Volume: Enterprises are awash with ever-growing data of all types, easily amassing terabytes—
even petabytes—of information.
Turn 12 terabytes of Tweets created each day into improved product sentiment analysis
Convert 350 billion annual meter readings to better predict power consumption
Velocity: Sometimes 2 minutes is too late. For time-sensitive processes such as catching fraud, big
data must be used as it streams into your enterprise in order to maximize its value.
Scrutinize 5 million trade events created each day to identify potential fraud
Analyze 500 million daily call detail records in real-time to predict customer churn faster
Variety: Big data is any type of data - structured and unstructured data such as text, sensor data,
audio, video, click streams, log files and more. New insights are found when analyzing these data
types together.
Monitor 100’s of live video feeds from surveillance cameras to target points of interest
Exploit the 80% data growth in images, video and documents to improve customer
satisfaction
Veracity: 1 in 3 business leaders don’t trust the information they use to make decisions. How can
you act upon information if you don’t trust it? Establishing trust in big data presents a huge
challenge as the variety and number of sources grows.


Our Objective

• Examine “cutting edge” marketing
programs which combine Social
channels and a marketing
database
• Discuss specific applications
impacting your organization


What are the Social Channels?
Public &
Private
Websites
Social Integrated
Programs Mobile

Social
Channels


Key Issues to Consider
Type of Data

Business Marketing
Value Use

Relationship Technology
Potential Use


SOCIAL MEDIA


Social Conversations are occurring all
of the time
• People are talking about
your company, your
competitors and different
topics continually
– However, all conversations
are not created equal
• You need to understand
the structure of social
media and ways to
monitor and use it


What is social media?
• Here is an example

• www.wefeelfine.org


WHAT IS SOCIAL ENGAGEMENT?
Most companies don’t understand the structure of the social cloud & the
database implications of different levels of engagement


Most marketers don’t understand this
basic equation

Social Social
Networks Communities


Social Insight 1
Social Networks Social Communities
• Where we talk about • Where we talk about our
everything Passions and interests
• What are examples of social • What are examples of social
networks? communities?


Social Networks Social Communities


Social Networks are not great for selling people
anything
• Noise
• Conflicting Interests
• Multiple Discussions

• Really can’t focus on
the individual & their
needs / motivations


Plus, on Social Networks, everyone is an expert

Think of the brand implications

Altimeter research 2012

Another Problem is these social networks don’t [can’t]
link to your critical business metrics

Social Investment

Visitors
Thumbs Up Profits Unmeasured
???
Inquiries

Travelers

Revenue Unknown

19

What are the social networks & data
implication?
• Analytics
– There are external systems designed
to analyze social communications
• Marketing
– Intercept marketing allows marketing
to join a conversation indicating
purchase interests
• Most network conversations are
not databased by companies


Social Analytics Resources
• www.Socialmention.com
– Very nice, free system showing conversations for any
search field you want
• Company & its products
• Competitors
• Topics used by your high value markets
• www.listorious.com
– Identifies the stronger blogger who influence
community conversations
• www.alltop.com
– Follows blogs and key websites discussing specific
topics

Advanced Social Monitoring
• Radian6, Netbase,
Chrimson Hexigon
• Social monitoring
• Intercept marketing
– CRM systems integration


Social monitoring systems listen to conversations across the
social cloud using APIs

Forum
Blogs
Boards

LinkedIn Video Sites

Social News
Twitter
Sites

Social Social
Facebook Media Aggregation
Monitoring Sites


Automatics Programming Interface
[API]
• www.boardreader.com

• https://dev.twitter.com/docs/streaming-apis
• http://developers.facebook.com/


Social Monitoring Systems

You need to know:
• Are they social?
• What social media are they using & how much?
• Who are the influential bloggers & how influential are they?
Radian6 word clouds
• And other great stuff!

AH HA MOMENT

What is the MOST FREQUENT
word PAIRED with Registry in the
Wedding Social Market?

SHOWER
GEEYEE Analysis


Traveling with Kids Word Cloud


SOCIAL COMMUNITIES ARE
DIFFERENT


Social Communities are different

Topic Trusted Engagement
Focused Experts Critical

Seeking Community
Answers focused


Important – Private communities
dwarf Public communities

Public
Communities
Private
Communities

Social Community also engage on many different levels
Profile
Facebook Twitter LinkedIn Based
StumbleUpon Pinterest Digg News &
Reddit Del.icio.us Bookmarking

Blogs MSN Huffington Post Thought Leadership
E News E Journals / Expert

Flickr YouTube Media Sharing

Forums Company
Communities Private Sites

Marketing Synergy, Inc MSINetwork.com #DMA2012 30

Deploy social the same way you sell your travel
prospects normally

To “sell” people, we
The dialog should build
need a place where we
the relationship over
can engage in a dialog
time
meaningful to them

I want to engage with I don’t want everyone
people with similar to hear our
interests conversation


Social Communities can become key
resources for your business

Interests Relationships

Activities Sales
Social
community


Social Community hierarchy
Profile
Facebook Twitter LinkedIn Based
StumbleUpon Pinterest Digg News &
Reddit Del.icio.us Bookmarking

Blogs MSN Huffington Post Thought Leadership
E News E Journals / Expert

Flickr YouTube Media Sharing

Forums Company
Communities Private Sites


Social Community hierarchy
Profile
Broad discussions with everyone Based
Lifestyles & Passions to link to News &
Key communities Bookmarking
Establish your expertise to
Your high value Thought Leadership
communities / Expert
Make your point
visible Media Sharing

Where the marketing
occurs Private Sites


Make it a “gated” community


Why do this?

Marketing Opt-
Relationship
in

Metrics Personalization
Info
Exchange


Examples
• https://www.ridgidforum.com/forum/forum.php
• http://www.facebook.com/membersproject
• http://www.emersonnetworkpower.com/en-
US/Pages/Default.aspx


Social Data

Web Social
Usage Usage

Profile Social
Data Sources
MDB

Websites are becoming more database driven

WEB MARKETING


Web Strategy
• Create high value
personas
• Identify them when
they first interact with
Adobe
• Customize the ENTIRE
WEB EXPERIENCE based
on personas and their
purchase tendencies


The methodology
Build High Value Personas

Determine information required to identify
persona membership

Determine persona’s product purchase
lifecycle

Offer White Papers & Other Enticements
requiring an Information Exchange

Add persona and personal information to the
marketing database & on a cookie for user


The Team
Jennifer - CMO
•Work towards key metrics that was collectively determined to be important to the CFO as well as the
CEO - metrics must be tangible and bottom-line driven, such as customer costs and customer value.
• Tech –savvy and always connected – whether on Email, Twitter, Blogs, Conferences
• CMOs will invest more in social media this year than ever before
• 65% of marketing leaders point to digital and social marketing as the primary driver behind their next
organizational structure
•73% of CEOs think marketers lack business credibility and are unable to deliver ROI
Mr. White
Ryan - Business Intelligence Analyst
•Analyzing, managing and transforming raw data into actionable insight for informed decision making
• Uses professional forums (such as Business Intelligence & Analytics Group on LinkedIn) to network and
get answers to highly technical and strategic questions.
• More of a politician than an analyst when helping management and analysts and marketers decide
on best metrics, derivations and calculations.
•Constantly slowed down by Untamed business processes between departments and between packaged
applications that require closely coordinated human and system supports.

Mr. Blue Steve – Search Analyst Mr. Orange

•Identify and fix problems: content gaps, language misalignment, interface problems and other
technology issues.
•Analytical and data-driven.
•Consider the time tension a big challenge in his work – “if you have more time, you can sure better
optimize the results”.
•Interested in the integration of social media and keyword searches and the measurement of the synergy
results.
•Consider the lack of experienced search analysts a problem to many company.

http://www.omniture.com/en/


Adobe Digital Marketing Strategy
A/B Split Testing

Product Lifecycle

Purchase Intent

Accelerate Purchase Funnel


Other Strategies
Custom social network where customers source ideas/content
• Microsite: My Starbucks Idea

Social media has evolved into a critical relationship builder,
integrated into all business units
• Multiple Twitter handles, network of blogs, Dell Island on
Second Life, Listening Command Center, microsite

Spread Social Media Responsibilities Across The Organization To
Empower Employees And Strengthen The Brand

Disney Promotes Moms Into Mass –Influencing Advocates
and Building Mom Community


MOBILE APPLICATIONS


Database Implications
• Database becomes a secure location of
information and services desired by the
individual
• Mobile accesses database 24x7
• Database continually monitors activities and
usage
• Database is source of opt-in communications
permissions


Mobile Application

• Demographic information
• My order list
• Pick up & Delivery Check
and time
• My Specials
• About
• Easy Baking
• Store Locations
• Offers/Coupons

Helps busy mothers/couples save time with
regards to order, pick up and bake

My Current Order
- Customers order cart
My order list - View Cart  Continue to Check out
My Order List
- Customers can order from their past purchase list
- Order from specials for the day/month/season
- Order from the ‘new arrivals’

Welcome, Jaewon

Shop My Order Lists

Order From My Past Purchases

Favorites

More

More
Provide functions that save the
time to order

Pick-up/Delivery Check

Pick Up Times
Cart Pick up Times
Customers can check a time that they deem
Please select a pick up date/time. convenient to pick up the pizza. A default 10
minute pick up time slot is considered.
Available Selected Holiday

My Past Purchases
Promotions
Previous Orders - If customer pick it up at not busy time, customers
12:30PM – 12:40PM
can save $1.00 0r have free new pizza sample
12:40PM – 12:50PM - New product promotions
Save $1.00
12:50PM – 13:00PM Or Free side
dish

More
Provide functions that save the time to
13:10PM – 13:20PM
pick up and save money as well.


Top Hottest Celebrities’ Customized Pizza
Customers can refer to top hottest celebrities'
My Specials customized Pizza without spending to build on
themselves one by one

My Special
Customers who want to make the pizza by themselves
Cart
can build it easily in User Friendly Interface of Mobile
My Homemade Pizza
App.

Our Hottest Customized Pizza Cart My Special

Pizza / Seasonal
Baking Tips / New organic Ingredients
Spring Farm

Build on Yours

Special Tips Side Dish

Customers can refer to the Baking Tips Cutie Pie Kit
and Useful Info.


About & Easy Promotion Events News
Baking Top Hottest Customized Pizza
-Customers can get the latest online/offline promotion
- Customers can refer to get participate and win the prize
events information, and top hottest customized Pizza
through Mobile App. build on themselves one by one
without spending to
- Customers can easily use all prizes(Coupon, Gift card etc)
through the Mobile App.

About HomeMade Pizza Co. Social Media
- Customers can navigate to the Homemade Pizza
microsite through the Mobile App.

Baking Time
Real Homemade About
Stories

Healthy Ingredients

Real Homemade
Stories

Healthy Ingredients

Pizza Baking Baking About Twitter
Time
View our Twitter feed and get all the
Start Timer
latest healthy and fresh pizza news

Homemade Pizza Story Pizza Baking Baking About
- Customers can useful and the latest Time
Baking Timer
Information about Homemade Pizza - Customers can use Baking timer to
Marketing Synergy, Inc MSINetwork.com #DMA2012 bake it

Becoming Exceptional


Summary
• Social Channels are becoming highly
integrated with marketing databases
• In addition, there are expert systems which
are necessary to monitor social media
• Trend is towards integration…not away from it
• Keep marketing databases flexible to
accommodate change


Questions?
Cracking the code
toRandy Hlavac
Twitter: @randyhlavac
Social IMC: #NUSocialIMC
630.328.9550


DMA 2012 Database Post Intensive

Recommended Sources for
Database Marketing, CRM and Integrated Marketing

The following lists include Pegg Nadler’s personal recommendations for
information and reference material in your day-to-day database marketing
activities. Many of the books listed are a part of my standard professional
library. Some of the older titles are DB classics and provide an excellent
framework for solid database marketing, best practices and guidance on
DBM processes.

Websites, Magazines, Newspapers & E-newsletters

Ad Age www.adage.com

B to B www.btobonline.com

Chief Direct Marketer www.chiefmarketer.com

Colloquy www.colloquy.com

CRM www.destinationcrm.com

Customer Think www.customerthink.com

Direct www.directmag.com

Direct Marketing News www.dmnews.com

DMA www.the-dma.org

Marketing Profs www.marketingprofs.com

Marketing Sherpa www.marketingsherpa.com

1 to 1 www.1to1.com

Target Marketing www.targetonline.com

Books

Arikan, Akin, Multichannel Marketing: Metrics and Methods for On and Offline Success,
Sybex, 2008

Baier, Martin and Riuf, Kurtis and Chakraborty, Goutam, Contemporary Database
Marketing, Racam Communications, 2002

Berry, Michael and Linoff, Gordon, Mastering Data Mining, Wiley, 2000

Brown, Stanley and Gulycz, Moosha, Performance Driven CRM, Wiley, 2002

Burnett, Ed, Database Marketing: The New Profit Frontier, Morris Lee Publishing, 1996

Cooper, Kenneth Carlton, The Relational Enterprise, American Management
Association, 2002

Cross, Richard and Smith, Janet, Customer Bonding, NTC Business Books, 1995

Curry, Jany and Curry, Adam, The Customer Marketing Method, Free Press, 2000

Curry, Kay, Know Your Customers!, Kogan Page Ltd., 1992

Deloitte & Touche, Managing Database Marketing Technology for Success, Direct
Marketing Association, 1992

Drozdenko, Ronald and Drake, Perry, Optimal Database Marketing, Sage Publications,
2002

Dyche, Jill, The CRM Handbook, Addison-Wesley, 2002

Paul W. Farris, Neil T. Bendle, Phillip E. Pfeifer and David J. Reibstein,
Marketing Metrics: The Definitive Guide to Measuring Marketing Performance (2nd
Edition), Pearson Prentice Hall, 2010

Francese, Peter, Capturing Customers, American Demographic Books, 1990

Franks, Bill, Taming the Big Data Tidal Wave, Wiley and SAS Business Series, 2012

Freeland, John, The Ultimate CRM Handbook, McGraw-Hill, 2003

Godin, Seth, Permission Marketing, Simon & Schuster, 1999

Gordon, Ian, Relationship Marketing, Wiley & Sons Canada, 1998

Greenberg, Paul, CRM at the Speed of Light, McGraw-Hill, 2002

Hartmann, Kenneth, Research and the Customer Lifecycle, Direct Marketing
Association, 1995

Hughes, Arthur, The Customer Loyalty Solution, McGraw-Hill, 2003

Hughes, Arthur, Strategic Database Marketing (4th edition), McGraw Hill, 2012 release

Jackson, Rob and Wang, Paul, Strategic Database Marketing, NTC Business Books,
1995

Jeffrey, Mark, Data-Driven Marketing, Wiley, 2010

Lee, Dick, The Customer Relationship Management Survival Guide, HYM Press, 2000

Nash, Edward, Database Marketing, McGraw Hill, 1993

Newburg, Jay and Marcus, Claudio, Target Smart!, Oasis Press, 1996

Newell, Frederick, loyalty.com, McGraw Hill, 2000

Newell, Frederick, The New Rules of Marketing, McGraw Hill, 1997

Nykamp, Melinda, The Customer Differential, American Management Association, 2001

Peck, Mark, Integrated Account Management, American Management Association, 1997

Peppers, Don and Rogers, Martha, Enterprise One to One, Currency Doubleday, 1997

Peppers, Don and Rogers, Martha, Extreme Trust: Honesty as a Competitive
Advantage, Portfolio, 2012

Peppers, Don and Rogers, Martha, Managing Customer Relationships: A Strategic
Framework, Wiley, 2011

Peppers, Don and Rogers, Martha, The One to One Fieldbook, Currency Doubleday,
1999

Peppers, Don and Rogers, Martha, The One to One Future, Currency Doubleday, 1993

Pine II, B. Joseph and Gilmore, James, The Experience Economy, Harvard Business
School Press, 1999

Raphel, Murray and Raphel, Neil, Up the Loyalty Ladder, HarperCollins, 1995

Roman, Ernan, Integrated Direct Marketing, NTC Business Books, 1995

Roman, Ernan, Voice of the Customer Marketing, McGraw Hill, 2010

Schmidt, Jack and Weber, Alan, Desktop Database Marketing, NTC Business Books,
1998

Shaver, Dick, The Next Step in Database Marketing, Wiley, 1996

Shepard, David, The New Direct Marketing, McGraw Hill, 1999

Seybold, Patricia, The Customer Revolution, Crown Business, 2001

Smith, Ellen Reid, e-loyalty, Harper Business, 2000

Swift, Ronald, Accelerating Customer Relationships, Prentice Hall PTR, 2001

Tooker, Richard, The Business of Database Marketing, Racom, 2006

Vavra, Terry, Aftermarketing: How to Keep Customers for Life Through Relationship
Marketing, Irwin, 1992

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